{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e017c9a3",
   "metadata": {},
   "source": [
    "# Learning to solve nonlinear ODE reference tracking problem\n",
    "\n",
    "Using [Differentiable predictive control (DPC) method](https://www.sciencedirect.com/science/article/pii/S0959152422000981) to learn parametric neural control policy for reference tracking of nonlinear ordinary differential equations (ODE).\n",
    "\n",
    "Example adopted from: https://apmonitor.com/do/index.php/Main/LevelControl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3ffdcd",
   "metadata": {},
   "source": [
    "## NeuroMANCER and Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bac7825",
   "metadata": {},
   "source": [
    "### Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa12921d",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install neuromancer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3995d5f",
   "metadata": {},
   "source": [
    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a9b6ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "\n",
    "import neuromancer.psl as psl\n",
    "from neuromancer.system import Node, System, SystemPreview\n",
    "from neuromancer.modules import blocks\n",
    "from neuromancer.modules.activations import activations\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.trainer import Trainer\n",
    "from neuromancer.dynamics import ode, integrators\n",
    "from neuromancer.plot import pltCL, pltPhase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfe1b7ed",
   "metadata": {},
   "source": [
    "## System model \n",
    "\n",
    "Lets consider the following system of two connected tanks controlled by a single pump and a two way valve.\n",
    "The system is a simplified model of a [pumped-storage hydroelectricity](https://en.wikipedia.org/wiki/Pumped-storage_hydroelectricity) which is a type of hydroelectric energy storage used by electric power systems for [load balancing](https://en.wikipedia.org/wiki/Load_balancing_(electrical_power). \n",
    "\n",
    "**System schematics**:  \n",
    "<img src=\"../figs/two_tank_level.png\" width=\"250\">  \n",
    "\n",
    "**System model**:  \n",
    "The system dynamics is defined by following nonlinear ordinary differential equations (ODEs):\n",
    "$$\n",
    " \\frac{dx_1}{dt} = c_1 (1.0 - v)  p - c_2  \\sqrt{x_1}  \\\\  \n",
    " \\frac{dx_2}{dt}  = c_1 v p + c_2  \\sqrt{x_1} - c_2 \\sqrt{x_2}\n",
    "$$  \n",
    "With system states $x_1$, and $x_2$ representing liquid levels in tank 1 and 2, respectively. Control actions are pump modulation $p$, and valve opening $v$. The ODE system is parametrized by inlet and outlet valve coefficients $c_1$ and $c_2$, respectively.\n",
    "\n",
    "**Control ojective**:   \n",
    "The objective is to control the tank levels into desired reference values by modulating the pump and valve control actions.\n",
    "\n",
    "System model and image adopted from: https://apmonitor.com/do/index.php/Main/LevelControl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd824de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ground truth system model\n",
    "gt_model = psl.nonautonomous.TwoTank()\n",
    "# sampling rate\n",
    "ts = gt_model.params[1]['ts']\n",
    "# problem dimensions\n",
    "nx = gt_model.nx    # number of states\n",
    "nu = gt_model.nu    # number of control inputs\n",
    "nref = nx           # number of references\n",
    "# constraints bounds\n",
    "umin = 0\n",
    "umax = 1.\n",
    "xmin = 0\n",
    "xmax = 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259ed294",
   "metadata": {},
   "source": [
    "# Differentiable Predictive Control \n",
    "\n",
    "Next we show how to solve the corresponding parametric optimal control using the [DPC method](https://www.sciencedirect.com/science/article/pii/S0959152422000981) implemented in Neuromancer.\n",
    "\n",
    "**Schematics of the Differentiable Predictive Control method**:  \n",
    "<img src=\"./figs/DPC_simple_method.png\" width=\"600\">  \n",
    "\n",
    "**Neural control policy**:  \n",
    "The objective of this tutorial is to learn neural control policy $u_k = \\pi(x_k, R)$ to control the tank levels by modulating the pump and valve control actions $u_k = [p_k, v_k]$. The policy takes in the measurements of system states $x_k$ at thime $k$ and desired references $R = [r_k, ..., r_{k+N}]$ over pre-defined horizon $N$. This tutorial shows how to construct a DPC policy in scenarios where future references $[r_{k+1}, ..., r_{k+N}]$ are known and where they are unknown, i.e $[r_{k+1},\\dots,r_{k+N}]=r_k$.\n",
    "\n",
    "**Differentiable system model**:  \n",
    "The DPC is a model-based policy optimization algorithm, that exploits the differentiability of a wide class of model representations for dynamical systems, including differential equations, state-space models, or various neural network architectures. In this example, we compactly represent the system model by ODE equations  $\\text{ODESolve}(f(x^i_k, u^i_k))$  describing the governing dynamics of the controlled system. \n",
    "\n",
    "**Differentiable predictive control problem formulation**:  \n",
    "We learn the explicit neural control policy by solving the following parametric optimal control problem: \n",
    "$$\n",
    "\\begin{align}\n",
    "&\\underset{\\theta}{\\text{minimize}}     && \\sum_{i=1}^m  \\Big( \\sum_{k=1}^{N-1} Q_x||x^i_k - r^i_k||_2^2  + Q_N||x^i_N - r^i_N||_2^2 \\Big) \\\\\n",
    "&\\text{subject to}    && x^i_{k+1} =  \\text{ODESolve}(f(x^i_k, u^i_k)) \\\\\n",
    "&                     && u^i_k = \\pi_{\\theta}(x^i_k, R^i) \\\\\n",
    "&                     && 0 \\le x^i_k \\le 1 \\\\\n",
    "&                     && 0 \\le u^i_k \\le 1 \\\\\n",
    "&                     && x^i_0 \\sim \\mathcal{P}_{x_0} \\\\\n",
    "&                     && R^i \\sim  \\mathcal{P}_R\n",
    "\\end{align}\n",
    "$$  \n",
    "The objective function is to minimize the reference tracking error $||x^i_k - r^i_k||_2^2$ over pre-defined prediction horizon $N$ weighted by a scalar $Q_x$, including terminal penalty weighted by $Q_N$.  The parametric neural control policy is given by $\\pi_{\\theta}(x^i_k, R^i)$. The neural control policy is optimized over a problem parameters sampled from the distributions $\\mathcal{P}_{x_0}$, and $\\mathcal{P}_R$, for state initial conditions, and references, respectively. The parameters $\\theta$ are optimized with stochastic gradient descent."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa938158",
   "metadata": {},
   "source": [
    "## Training dataset generation\n",
    "\n",
    "For a training dataset we randomly sample initial conditions of states and sequence of admissible reference trajectories over predefined prediction horizon from given distributions $\\mathcal{P}_{x_0}$, and $\\mathcal{P}_R$, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b74ee8c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "nsteps = 30  # prediction horizon\n",
    "n_samples = 2000    # number of sampled scenarios\n",
    "\n",
    "#  sampled references for training the policy\n",
    "list_refs = [torch.rand(1, 1)*torch.ones(nsteps+1, nref) for k in range(n_samples)]\n",
    "ref = torch.cat(list_refs)\n",
    "batched_ref = ref.reshape([n_samples, nsteps+1, nref])\n",
    "# Training dataset\n",
    "train_data = DictDataset({'x': torch.rand(n_samples, 1, nx),   # sampled initial conditions of states\n",
    "                          'r': batched_ref}, name='train')\n",
    "\n",
    "# sampled references for development set\n",
    "list_refs = [torch.rand(1, 1)*torch.ones(nsteps+1, nref) for k in range(n_samples)]\n",
    "ref = torch.cat(list_refs)\n",
    "batched_ref = ref.reshape([n_samples, nsteps+1, nref])\n",
    "# Development dataset\n",
    "dev_data = DictDataset({'x': torch.rand(n_samples, 1, nx),    # sampled initial conditions of states\n",
    "                        'r': batched_ref}, name='dev')\n",
    "\n",
    "# torch dataloaders\n",
    "batch_size = 200\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,\n",
    "                                           collate_fn=train_data.collate_fn,\n",
    "                                           shuffle=False)\n",
    "dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=batch_size,\n",
    "                                         collate_fn=dev_data.collate_fn,\n",
    "                                         shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a41c08bd",
   "metadata": {},
   "source": [
    "## ODE system model\n",
    "\n",
    "In the following, we instatiate a continous-time system model together with a 4th order Ruge-Kutta numerical integrator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b3f2c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# white-box ODE model with no-plant model mismatch\n",
    "two_tank_ode = ode.TwoTankParam()                   # ODE system equations implemented in PyTorch\n",
    "two_tank_ode.c1 = nn.Parameter(torch.tensor(gt_model.c1), requires_grad=False)\n",
    "two_tank_ode.c2 = nn.Parameter(torch.tensor(gt_model.c2), requires_grad=False)\n",
    "\n",
    "# integrate continuous time ODE\n",
    "integrator = integrators.RK4(two_tank_ode, h=torch.tensor(ts))   # using 4th order runge kutta integrator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0387d6a2",
   "metadata": {},
   "source": [
    "## System model and Control policy in Neuromancer\n",
    "\n",
    "Here we construct a closed-loop systems as differentiable computational graphs by connecting the system dynamics model  $x_{k+1} = \\text{ODESolve}(f(x_k, u_k))$ with neural control policies $u_k = \\pi_{\\theta}(x_k, r_k)$ or $u_k = \\pi_{\\theta}(x_k, R)$, in case future reference is known. Hence we obtain a trainable system architectures: \n",
    "\n",
    "1. Without preview of future reference $x_{k+1} = \\text{ODESolve}(f(x_k, \\pi_{\\theta}(x_k, r_k)))$,\n",
    "2. With preview of future reference $x_{k+1} = \\text{ODESolve}(f(x_k, \\pi_{\\theta}(x_k, R)))$.\n",
    "\n",
    "Notice how size of input layers differs in case 1 case 2.\n",
    "\n",
    "### No preview of future reference:\n",
    "\n",
    "$\\pi_\\theta : \\mathbb{R}^{n_x\\times n_r} \\mapsto \\mathbb{R}^{n_u}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "149f7bc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# symbolic system model\n",
    "model = Node(integrator, ['x', 'u'], ['x'], name='model')\n",
    "\n",
    "# neural net control policy with hard control action bounds\n",
    "net = blocks.MLP_bounds(insize=nx + nref, outsize=nu, hsizes=[32, 32],\n",
    "                    nonlin=activations['gelu'], min=umin, max=umax)\n",
    "policy = Node(net, ['x', 'r'], ['u'], name='policy')\n",
    "\n",
    "# closed-loop system model\n",
    "cl_system = System([policy, model], nsteps=nsteps)\n",
    "# cl_system.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73e1a02",
   "metadata": {},
   "source": [
    "### With preview of future reference:\n",
    "\n",
    "$\\pi_\\theta : \\mathbb{R}^{n_x\\times (N+1)\\cdot n_r} \\mapsto \\mathbb{R}^{n_u}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "98edb20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# neural net control policy with reference preview\n",
    "net_preview = blocks.MLP_bounds(insize=nx + (nref*(nsteps+1)), outsize=nu, hsizes=[64, 32],\n",
    "                    nonlin=activations['gelu'], min=umin, max=umax)\n",
    "policy_with_preview = Node(net_preview, ['x', 'r'], ['u'], name='policy')\n",
    "\n",
    "cl_system_preview = SystemPreview([policy_with_preview, model], name='cl_system_preview',\n",
    "                nsteps=nsteps, preview_keys_map={'r': ['policy']}, # reference preview for neural control policy node\n",
    "                preview_length={'r': nsteps}, pad_mode='replicate') # replicate last sample in the sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b33d60",
   "metadata": {},
   "source": [
    "## Differentiable Predictive Control objectives and constraints\n",
    "\n",
    "Here we take advantage of Neuromancer's high level symbolic language to define objective and constraint terms of our optimal control problem. Furthermore, we utilize a penalty method for constraint relaxation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3eccb0de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables\n",
    "x = variable('x')\n",
    "ref = variable(\"r\")\n",
    "# objectives\n",
    "regulation_loss = 5. * ((x == ref) ^ 2)  # target posistion\n",
    "# constraints\n",
    "state_lower_bound_penalty = 10.*(x > xmin)\n",
    "state_upper_bound_penalty = 10.*(x < xmax)\n",
    "terminal_lower_bound_penalty = 10.*(x[:, [-1], :] > ref-0.01)\n",
    "terminal_upper_bound_penalty = 10.*(x[:, [-1], :] < ref+0.01)\n",
    "# objectives and constraints names for nicer plot\n",
    "regulation_loss.name = 'state_loss'\n",
    "state_lower_bound_penalty.name = 'x_min'\n",
    "state_upper_bound_penalty.name = 'x_max'\n",
    "terminal_lower_bound_penalty.name = 'y_N_min'\n",
    "terminal_upper_bound_penalty.name = 'y_N_max'\n",
    "# list of constraints and objectives\n",
    "objectives = [regulation_loss]\n",
    "constraints = [\n",
    "    state_lower_bound_penalty,\n",
    "    state_upper_bound_penalty,\n",
    "    terminal_lower_bound_penalty,\n",
    "    terminal_upper_bound_penalty,\n",
    "]\n",
    "# create constrained optimization loss\n",
    "loss = PenaltyLoss(objectives, constraints)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d77bde",
   "metadata": {},
   "source": [
    "## Differentiable optimal control problems\n",
    "\n",
    "Here we put things together to construct a differentibale optimal control problems for both cases.\n",
    "\n",
    "### Problem without reference preview:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ab1f85c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct constrained optimization problem without reference preview\n",
    "# data (x_k, r_k) -> parameters (xi_k) -> policy (u_k) -> dynamics (x_{k+1})\n",
    "problem = Problem([cl_system], loss)\n",
    "# plot computational graph\n",
    "# problem.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48051bfe",
   "metadata": {},
   "source": [
    "### Problem with reference preview:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c89e38e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct constrained optimization problem with reference preview\n",
    "# data (x_k, [r_k, ..., r_{k+N}]) -> parameters (xi_k) -> policy (u_k) -> dynamics (x_{k+1})\n",
    "problem_with_preview = Problem([cl_system_preview], loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5ef578",
   "metadata": {},
   "source": [
    "## Solve the problem\n",
    "\n",
    "We solve the problems using stochastic gradient descent over pre-defined training data of sampled parameters.\n",
    "\n",
    "#### Solving the problem without reference preview:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9467aec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "epoch: 3  train_loss: 3.34563946723938\n",
      "epoch: 4  train_loss: 3.1257853507995605\n",
      "epoch: 5  train_loss: 2.808243751525879\n",
      "epoch: 6  train_loss: 2.3272194862365723\n",
      "epoch: 7  train_loss: 1.737731695175171\n",
      "epoch: 8  train_loss: 1.3425382375717163\n",
      "epoch: 9  train_loss: 1.1724798679351807\n",
      "epoch: 10  train_loss: 1.1051442623138428\n",
      "epoch: 11  train_loss: 1.0458886623382568\n",
      "epoch: 12  train_loss: 1.004876971244812\n",
      "epoch: 13  train_loss: 0.971759021282196\n",
      "epoch: 14  train_loss: 0.9397326707839966\n",
      "epoch: 15  train_loss: 0.9083455204963684\n",
      "epoch: 16  train_loss: 0.8779774904251099\n",
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      "epoch: 22  train_loss: 0.7753738164901733\n",
      "epoch: 23  train_loss: 0.7666093707084656\n",
      "epoch: 24  train_loss: 0.7589672803878784\n",
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      "epoch: 26  train_loss: 0.7463878989219666\n",
      "epoch: 27  train_loss: 0.7415649890899658\n",
      "epoch: 28  train_loss: 0.7369214296340942\n",
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      "epoch: 69  train_loss: 0.6710808277130127\n",
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      "epoch: 71  train_loss: 0.669707715511322\n",
      "epoch: 72  train_loss: 0.6691303253173828\n",
      "epoch: 73  train_loss: 0.6685208082199097\n",
      "epoch: 74  train_loss: 0.6679416298866272\n",
      "epoch: 75  train_loss: 0.6673305630683899\n",
      "epoch: 76  train_loss: 0.666858434677124\n",
      "epoch: 77  train_loss: 0.666538417339325\n",
      "epoch: 78  train_loss: 0.6660668849945068\n",
      "epoch: 79  train_loss: 0.6655570864677429\n",
      "epoch: 80  train_loss: 0.6650618314743042\n",
      "epoch: 81  train_loss: 0.6646315455436707\n",
      "epoch: 82  train_loss: 0.6642264127731323\n",
      "epoch: 83  train_loss: 0.6638265252113342\n",
      "epoch: 84  train_loss: 0.6633349657058716\n",
      "epoch: 85  train_loss: 0.6629056930541992\n",
      "epoch: 86  train_loss: 0.6625400185585022\n",
      "epoch: 87  train_loss: 0.662317156791687\n",
      "epoch: 88  train_loss: 0.661884605884552\n",
      "epoch: 89  train_loss: 0.6615703701972961\n",
      "epoch: 90  train_loss: 0.6611541509628296\n",
      "epoch: 91  train_loss: 0.6610023975372314\n",
      "epoch: 92  train_loss: 0.6605466604232788\n",
      "epoch: 93  train_loss: 0.6602798700332642\n",
      "epoch: 94  train_loss: 0.659863293170929\n",
      "epoch: 95  train_loss: 0.6596167087554932\n",
      "epoch: 96  train_loss: 0.6593341827392578\n",
      "epoch: 97  train_loss: 0.659048855304718\n",
      "epoch: 98  train_loss: 0.6587919592857361\n",
      "epoch: 99  train_loss: 0.6585110425949097\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer = torch.optim.AdamW(problem.parameters(), lr=0.002)\n",
    "#  Neuromancer trainer\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader, dev_loader,\n",
    "    optimizer=optimizer,\n",
    "    epochs=100,\n",
    "    train_metric='train_loss',\n",
    "    eval_metric='dev_loss',\n",
    "    warmup=50, \n",
    ")\n",
    "# Train control policy\n",
    "best_model = trainer.train()\n",
    "# load best trained model\n",
    "trainer.model.load_state_dict(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd55f161",
   "metadata": {},
   "source": [
    "#### Solving the problem with reference preview:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32fae0da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 3.303844451904297\n",
      "epoch: 1  train_loss: 2.189786195755005\n",
      "epoch: 2  train_loss: 1.735865592956543\n",
      "epoch: 3  train_loss: 1.4452097415924072\n",
      "epoch: 4  train_loss: 1.236545205116272\n",
      "epoch: 5  train_loss: 1.1166846752166748\n",
      "epoch: 6  train_loss: 1.0988012552261353\n",
      "epoch: 7  train_loss: 1.0865684747695923\n",
      "epoch: 8  train_loss: 1.0424336194992065\n",
      "epoch: 9  train_loss: 0.9725844264030457\n",
      "epoch: 10  train_loss: 0.9440538287162781\n",
      "epoch: 11  train_loss: 0.9775888323783875\n",
      "epoch: 12  train_loss: 0.969544529914856\n",
      "epoch: 13  train_loss: 0.9373958706855774\n",
      "epoch: 14  train_loss: 0.911830723285675\n",
      "epoch: 15  train_loss: 0.8644700050354004\n",
      "epoch: 16  train_loss: 0.8191527128219604\n",
      "epoch: 17  train_loss: 0.9025786519050598\n",
      "epoch: 18  train_loss: 0.9426630735397339\n",
      "epoch: 19  train_loss: 0.8582304120063782\n",
      "epoch: 20  train_loss: 0.7881128191947937\n",
      "epoch: 21  train_loss: 0.7589594125747681\n",
      "epoch: 22  train_loss: 0.8048115968704224\n",
      "epoch: 23  train_loss: 0.8599504232406616\n",
      "epoch: 24  train_loss: 0.9121084213256836\n",
      "epoch: 25  train_loss: 0.8414789438247681\n",
      "epoch: 26  train_loss: 0.802230954170227\n",
      "epoch: 27  train_loss: 0.7764452695846558\n",
      "epoch: 28  train_loss: 0.7460463643074036\n",
      "epoch: 29  train_loss: 0.7902456521987915\n",
      "epoch: 30  train_loss: 0.7883756160736084\n",
      "epoch: 31  train_loss: 0.7849921584129333\n",
      "epoch: 32  train_loss: 0.7997775077819824\n",
      "epoch: 33  train_loss: 0.7614902853965759\n",
      "epoch: 34  train_loss: 0.8018302917480469\n",
      "epoch: 35  train_loss: 0.7377609014511108\n",
      "epoch: 36  train_loss: 0.7558544874191284\n",
      "epoch: 37  train_loss: 0.7601315379142761\n",
      "epoch: 38  train_loss: 0.7446917295455933\n",
      "epoch: 39  train_loss: 0.7675703167915344\n",
      "epoch: 40  train_loss: 0.7752916216850281\n",
      "epoch: 41  train_loss: 0.7434061765670776\n",
      "epoch: 42  train_loss: 0.7122148871421814\n",
      "epoch: 43  train_loss: 0.7553150653839111\n",
      "epoch: 44  train_loss: 0.7563540935516357\n",
      "epoch: 45  train_loss: 0.7489089369773865\n",
      "epoch: 46  train_loss: 0.751783013343811\n",
      "epoch: 47  train_loss: 0.747988224029541\n",
      "epoch: 48  train_loss: 0.7700695991516113\n",
      "epoch: 49  train_loss: 0.7385033369064331\n",
      "epoch: 50  train_loss: 0.7226711511611938\n",
      "epoch: 51  train_loss: 0.6939368844032288\n",
      "epoch: 52  train_loss: 0.6984420418739319\n",
      "epoch: 53  train_loss: 0.7028792500495911\n",
      "epoch: 54  train_loss: 0.683489203453064\n",
      "epoch: 55  train_loss: 0.6935009956359863\n",
      "epoch: 56  train_loss: 0.6933167576789856\n",
      "epoch: 57  train_loss: 0.7282596826553345\n",
      "epoch: 58  train_loss: 0.7360263466835022\n",
      "epoch: 59  train_loss: 0.7824782133102417\n",
      "epoch: 60  train_loss: 0.7849501371383667\n",
      "epoch: 61  train_loss: 0.7481871843338013\n",
      "epoch: 62  train_loss: 0.7628939747810364\n",
      "epoch: 63  train_loss: 0.7041958570480347\n",
      "epoch: 64  train_loss: 0.6931723356246948\n",
      "epoch: 65  train_loss: 0.6854077577590942\n",
      "epoch: 66  train_loss: 0.6850457191467285\n",
      "epoch: 67  train_loss: 0.7124100923538208\n",
      "epoch: 68  train_loss: 0.7023735046386719\n",
      "epoch: 69  train_loss: 0.7714219093322754\n",
      "epoch: 70  train_loss: 0.7746549844741821\n",
      "epoch: 71  train_loss: 0.7757418155670166\n",
      "epoch: 72  train_loss: 0.7904804944992065\n",
      "epoch: 73  train_loss: 0.7009080648422241\n",
      "epoch: 74  train_loss: 0.6957990527153015\n",
      "epoch: 75  train_loss: 0.7312332987785339\n",
      "epoch: 76  train_loss: 0.8113037943840027\n",
      "epoch: 77  train_loss: 0.7805114984512329\n",
      "epoch: 78  train_loss: 0.7615134716033936\n",
      "epoch: 79  train_loss: 0.7217000722885132\n",
      "epoch: 80  train_loss: 0.7524735927581787\n",
      "epoch: 81  train_loss: 0.7286802530288696\n",
      "epoch: 82  train_loss: 0.7215177416801453\n",
      "epoch: 83  train_loss: 0.7238782644271851\n",
      "epoch: 84  train_loss: 0.7117579579353333\n",
      "epoch: 85  train_loss: 0.715262234210968\n",
      "epoch: 86  train_loss: 0.6882498264312744\n",
      "epoch: 87  train_loss: 0.6862824559211731\n",
      "epoch: 88  train_loss: 0.6795118451118469\n",
      "epoch: 89  train_loss: 0.6760023832321167\n",
      "epoch: 90  train_loss: 0.6925853490829468\n",
      "epoch: 91  train_loss: 0.6910882592201233\n",
      "epoch: 92  train_loss: 0.6780552268028259\n",
      "epoch: 93  train_loss: 0.6716234087944031\n",
      "epoch: 94  train_loss: 0.6792916059494019\n",
      "epoch: 95  train_loss: 0.6740278005599976\n",
      "epoch: 96  train_loss: 0.678796112537384\n",
      "epoch: 97  train_loss: 0.7409356832504272\n",
      "epoch: 98  train_loss: 0.9009270668029785\n",
      "epoch: 99  train_loss: 0.8403962254524231\n",
      "epoch: 100  train_loss: 0.7796652913093567\n",
      "epoch: 101  train_loss: 0.8419855833053589\n",
      "epoch: 102  train_loss: 0.8205050230026245\n",
      "epoch: 103  train_loss: 0.7458040714263916\n",
      "epoch: 104  train_loss: 0.6990516781806946\n",
      "epoch: 105  train_loss: 0.69588303565979\n",
      "epoch: 106  train_loss: 0.67508864402771\n",
      "epoch: 107  train_loss: 0.6783467531204224\n",
      "epoch: 108  train_loss: 0.6716516613960266\n",
      "epoch: 109  train_loss: 0.6707953810691833\n",
      "epoch: 110  train_loss: 0.6892908811569214\n",
      "epoch: 111  train_loss: 0.6795682311058044\n",
      "epoch: 112  train_loss: 0.6844571828842163\n",
      "epoch: 113  train_loss: 0.6715230941772461\n",
      "epoch: 114  train_loss: 0.6837347149848938\n",
      "epoch: 115  train_loss: 0.672432541847229\n",
      "epoch: 116  train_loss: 0.6846605539321899\n",
      "epoch: 117  train_loss: 0.7508401870727539\n",
      "epoch: 118  train_loss: 0.7463026642799377\n",
      "epoch: 119  train_loss: 0.7087539434432983\n",
      "epoch: 120  train_loss: 0.7190431356430054\n",
      "epoch: 121  train_loss: 0.717298150062561\n",
      "epoch: 122  train_loss: 0.6803185939788818\n",
      "epoch: 123  train_loss: 0.6680668592453003\n",
      "epoch: 124  train_loss: 0.6794125437736511\n",
      "epoch: 125  train_loss: 0.6822396516799927\n",
      "epoch: 126  train_loss: 0.7236447930335999\n",
      "epoch: 127  train_loss: 0.7400513887405396\n",
      "epoch: 128  train_loss: 0.7189072370529175\n",
      "epoch: 129  train_loss: 0.7391317486763\n",
      "epoch: 130  train_loss: 0.7933220267295837\n",
      "epoch: 131  train_loss: 0.7355253100395203\n",
      "epoch: 132  train_loss: 0.7169135808944702\n",
      "epoch: 133  train_loss: 0.6860517263412476\n",
      "epoch: 134  train_loss: 0.710307776927948\n",
      "epoch: 135  train_loss: 0.7283300757408142\n",
      "epoch: 136  train_loss: 0.7422869801521301\n",
      "epoch: 137  train_loss: 0.7094355821609497\n",
      "epoch: 138  train_loss: 0.711612343788147\n",
      "epoch: 139  train_loss: 0.6947157979011536\n",
      "epoch: 140  train_loss: 0.6793907284736633\n",
      "epoch: 141  train_loss: 0.6908987760543823\n",
      "epoch: 142  train_loss: 0.7061898708343506\n",
      "epoch: 143  train_loss: 0.682351291179657\n",
      "epoch: 144  train_loss: 0.71225506067276\n",
      "epoch: 145  train_loss: 0.6752222776412964\n",
      "epoch: 146  train_loss: 0.6643544435501099\n",
      "epoch: 147  train_loss: 0.6678594350814819\n",
      "epoch: 148  train_loss: 0.7160246968269348\n",
      "epoch: 149  train_loss: 0.819424033164978\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer_ = torch.optim.Adam(problem_with_preview.parameters(), lr=0.01, weight_decay=0.002)\n",
    "trainer_with_preview = Trainer(\n",
    "        problem_with_preview,\n",
    "        train_loader, dev_loader,\n",
    "        optimizer=optimizer_,\n",
    "        epochs=150,\n",
    "        train_metric='train_loss',\n",
    "        eval_metric='dev_loss',\n",
    "        warmup=5,\n",
    "        patience=50\n",
    "    )\n",
    "# Train control policy with reference preview\n",
    "best_model_preview = trainer_with_preview.train()\n",
    "# Load best model with reference preview\n",
    "trainer_with_preview.model.load_state_dict(best_model_preview)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c1360ed",
   "metadata": {},
   "source": [
    "# Evaluate best model on a system rollout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b3035dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the test data\n",
    "nsteps = 1000\n",
    "step_length = 250\n",
    "# generate reference\n",
    "np_refs = psl.signals.step(nsteps + 1, 1, min=xmin, max=xmax, randsteps=4, rng=np.random.default_rng(20))\n",
    "R = torch.tensor(np_refs, dtype=torch.float32).reshape(1, nsteps+1, 1)\n",
    "torch_ref = torch.cat([R, R], dim=-1)\n",
    "# generate initial data for closed loop simulation\n",
    "data = {'x': torch.rand(1, 1, nx, dtype=torch.float32),\n",
    "        'r': torch_ref}\n",
    "\n",
    "# constraints bounds\n",
    "Umin = umin * np.ones([nsteps, nu])\n",
    "Umax = umax * np.ones([nsteps, nu])\n",
    "Xmin = xmin * np.ones([nsteps+1, nx])\n",
    "Xmax = xmax * np.ones([nsteps+1, nx])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f99983",
   "metadata": {},
   "source": [
    "## Evaluation of best model without reference preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "937b9143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ALH2FJirFi5lK8fGy804A6DahOADQs+q639VC8XSl+FTVHfsAAAB5V60/gVy0YiFdKT46aU5xAOg2oTgAkKM5xaOuUrwc7tgHAADIu0oL26cXIx2KqxQHgO4TigMAuQnFk/Z3y/qHUuvKVRcnAAAA8q5Rp7HFKmUqxcemdCgDgG4TigMAOWp/V4jlA8tSayoqxQEAAHKvGpW2heITFeedANBtQnEAIEeV4hErB9Lt06sFFycAAADyLntT9eIj8Yi+TCg+PuW8EwC6TSgOAOSq/d3yulB8spNDAgAAYAmqn1F88bF4X3EgtTxZ0T4dALpNKA4A9LD69ncrB9Pt06MwGZVKdjsAAADypFJtXfv0ukrxskpxAOg2oTgA0LPqLmkU6tunFwqVGJ1ULQ4AAEBr9GcqxacqzjkBoNuE4gBAbuaES+70XzW0vG67reM7OzYmAAAA9o3zx1aF4pMVleIA0G1CcQAgRxc1IlYNpivFE1vHRjo0IgAAAPYFhaTV2CL1l9Lt0ydVigNA1wnFAYCeVY10KF6IQuzXoFJ829hoB0cFAADAUj9/zC7ujcHiYGp5qqpSHAC6TSgOAOSq/d3KBpXi27RPBwAAyLVGncYWa6CUmVO8qlIcALpNKA4A5KpSvK9UimqlL7V++4RKcQAAgDyrZEPxJtqnD5bSleJl7dMBoOuE4gBA7u70L1TT87ttHzenOAAAQJ7V31S9eAN96UrxcmifDgDdJhQHAHIjqRSf/pm+QLFjQigOAACQZ42m31qsoUz79HJ1atH7AgBaQygOAOTmTv+ZixqFavoCxcjkWAdHBQAAwL5y/rgYQ33p9umV0D4dALpNKA4A5OeixkNzwhUzleI7J80pDgAAkGvZ08cmdlUXileF4gDQbUJxACA37e9mLmqUCirFAQAA2KUSlcwaleIA0EuE4gBA7trf9dWF4irFAQAA8qxuSvEmQvFl/elQvFowpzgAdJtQHADIzUWNmUsa2VB8rKxSHAAAgNYYzlSKh0pxAOg6oTgA0LOq87S/Kxb6U2unKu7aBwAAyLNsp7FCM5XiA0PpfasUB4CuE4oDADmqFJ9pn96XWj9Zcdc+AABAntVPv9XCSnGhOAB0nVAcAOhh2VR8OhQvZUJxleIAAAD5Vq2bVHzxleIrMpXihUI1xiYnFr0/AKB5QnEAIHd3+gvFAQAA2H379MVb1p+dUzxi+/hYE3sEAJolFAcAcnOn/2z79GJ2TnHt0wEAAPKsrlC8mUrxwXSleGLnhFAcALpJKA4A5KhSfKZ9eim1dqqqUhwAACDfKi0LxZf3D9et2yEUB4CuEooDALm503/mkoZKcQAAAHYbiTfRP31lw0rx8cXvEABomlAcAMjRnHDTVzX6M6F4WaU4AABArmWn32qqffpAg1B8cnTR+wMAmicUBwByd1Gjr9iXWisUBwAAyLf688fF6yuVolpNT9ulUhwAuksoDgD0sEyl+EM3+qsUBwAAYHdmOo0tWjV9M/bIpDnFAaCbhOIAQG7ap89UiveXhOIAAADs+fxxsQqZUHx0UqU4AHSTUBwAyN2c4n2F9MWJilAcAAAg11rZPj1RiEyl+JRKcQDoJqE4ANCz6q9pTIfiA30DqbWVEIoDAADk2Xw3VS9WoZruUDY2OdHU/gCA5gjFAYAeVkktFR6aVHzAnOIAAAC0sVK8mKkUH1UpDgBdJRQHAHrWfNc0+ovpixPVKHdmQAAAAOwTmq0ULxYyleJTKsUBoJuE4gBAbtrfFR/66jOYbZ+uUhwAACDXsuePM9NvLVYxMqF4ebyp/QEAzRGKAwC5u6gxUEpfnKiaUxwAACDX6s4fH5p+a7FKmUrxcZXiANBVQnEAIDcXNWYuaWTnFNc+HQAAIN+yc4o3F4k3CMVVigNAVwnFAYDeVVcoPn1ZY7AvE4oXhOIAAAB51ur26aVCetquibJKcQDoJqE4AJCjSvHpixpDmTnFtU8HAACglZXifXWV4kJxAOgmoTgAkKM7/acNZkLx0D4dAAAg1zLd05uOxfuL6fPOyYpQHAC6SSgOAORoTrjpixoDJe3TAQAA2KUalZbur6+YPu+cMKc4AHSVUBwAyI3CQ3OKD2crxYXiAAAAuZatFJ+5qbpVleJT1cmm9gcANEcoDgDkplI85plTvFAoR6XS2qoAAAAA9iXZVLy5UHwg2z7dnOIA0FVCcQAgN3OKz1zSGOxLt7FLjJXdtQ8AAJBX2fPHZg2UVIoDwFIiFAcA8hOKz7RP7x+s23Z00l37AAAAeZXtNNZs+/RsKD5Zdc4JAN0kFAcAela1mm2JPs+c4hExMuECBQAAQF7VV4o3F4oPltI3Y5erU03tDwBojlAcAMiNmTv9G7ZPnxKKAwAA5FWmULzJSLy+UrysUhwAukooDgDkbk64hu3Tp8Y7MCIAAAD2iem3mozFh+oqxc0pDgDdJBQHAHI3p/jy/vr26aMTLlAAAAAwo8lQPDNtV0UoDgBdJRQHAHpWNdP/brZ9en99+/TxslZ2AAAAeVV3/thk//ShvqHUciXMKQ4A3SQUBwByYyYUHyrVh+Kj5hQHAADIscbTb7WqUrwaKsUBoJuE4gBA7uYULxaLUa2UUuvGJoXiAAAAeVV//thcqfiy/vSc4tWCSnEA6CahOADQw7Lt7+Z+9UmH4uNld+0DAADkVTYUn+k0tlhDfZlQXKU4AHSVUBwAyM2ccHMVqplQXPt0AACA3Ko/fWwuFF/en55TXKU4AHRXLkLxu+66K971rnfFk5/85DjssMNicHCw9jNZTtYnr7fbL3/5y/jrv/7rOP300+OQQw6JoaGh2iN5ftppp8X5558f1157bdvHAQB5Ut/8bu5Fjb7Ua2NT7toHAADIr0pqqVBoLhQfHkhXiodQHAC6Kn01uAf98z//c/zVX/1V7Ny5M7X+7rvvrj2+//3vx9vf/vZ473vfGy996Utb/vlbt26NV7ziFfGxj32s4ev33ntv7XH55ZfXAvo//uM/jn/4h3+I/fbbr+VjAYD8mb/9XVIpPvfVibJKcQAAgLzaTaOxllSKFwqVmJiaioG+nr8kDwBLUk//Bn7rW98ab3rTm1LrNmzYEIceemjceeedcdNNN9XW7dixI172spfFAw88EG94wxta9vnbtm2L3/zN34wrr7wytf6oo46K9evX11q63nrrrXH77bfPvvaJT3wifvGLX8Sll14aa9asadlYACCPsu3T597pX4h0KD6uUhwAACC3Wj2n+PKBdCie2D4xGvv3rWxqvwDA4vRs+/QLL7wwFYifeOKJtXD6V7/6VVx22WVx4403xhVXXBEnnHDC7DZvfOMb46KLLmrZGJJ26XMD8aRN+tVXXx233HJLbQzf/va347bbbouf/OQnceqpp85u99Of/jRe85rXtGwcAJBX2YsacxUy9waOl4XiAAAAedXqUHy4b6Bu3eik804A6JaeDMUnJydrLdNnHH744fHd7343HvOYx6S2O+WUU2rrk/nFZyTvm5pqfn6X7du3xwUXXDC7/NjHPjYuueSSePjDH1637aMe9aj4+te/Ho9+9KNn13384x+vtV4HAFonXSmeDsUnKy5OAAAAMKPJULy/USg+3tQ+AYDF68lQ/DOf+UytEnzG3/7t3847R/fatWtrr8+44YYbau9v1g9/+MMYGxubXX7ta18bg4OD824/NDQU559//uzyxMRE/OhHP2p6HACQZ7u7079YKKVeG58ypzgAAEBe1Z8/NmewQaW4absAoHt6MhT/7Gc/O/s8mT/8Oc95zm63P+ecc+KQQw6ZXf7c5z7X9Bjuv//+1PIjHvGIPb4nu82DDz7Y9DgAINequwnFM5XiEyrFAQAA8mv+2bcWZXmjSnE3YwNA1/RcKD46OlprRT7jrLPOir6+9EXvrOT1ZLsZX/va11JV3ouxcuXK1PJC9pfdZr7qdgBgkdc05tzqXyxkQnFzigMAAORWtlI85ky/tRiD/f1168YmheIA0C09F4pfd911MT6+a26WJz/5yQt639ztknA62U8zHv/4x0exuOuv97LLLtvjey699NLZ50mr9V//9V9vagwAwO4qxdPt0yeF4gAAALm1u+m3FmOo1CAUVykOAF3Tc6H4Nddck1resGHDgt6X3e7aa69tahwHHXRQPP/5z59d/pu/+Zu45ZZb5t3+pptuine84x2zyy996UtVigNAGy9qlDKV4pOVqY6NCwAAgKWlmpl+q1lJwVS1kr4ZWygOAN2z+77i+6Bbb701tXzkkUcu6H3r169PLe8uwF6o97///XHVVVfFL3/5y9oc46ecckq87nWvi2c961mzn5d8zoUXXhjvfOc7Y8uWLbV1z372s2vLC5FUxc+tjN+2bVvt5+TkZO0BwNIzc3x2nO7CRY3qrr/3uvbpU+P+TYAUx2uApc+xGmhnKN78sSUJxcuzSzvHx3J5vHKsBtg3TPb48brnQvGZUHjGmjVrFvS+1atXp5a3b9/e9FjWrVsX3/ve9+KVr3xlfPKTn4xNmzbFq1/96tqjkSTA//M///P4y7/8y1Tr9d1Jqsvf8pa3NGzFvmzZsqb/DAC0z9e//vVuD6HnjYyORAztWt68ZUt89atfrT2fGJtMvXb/xvtnXwOYy/EaYOlzrAaatXXr1tQ54sjOkebPEavpSvGrrv5JDN+2OfLKsRpg33DpnOmee0nPheI7duxILQ8PDy/ofdntWhGKJ9auXRsf/ehH49RTT43zzz+/FozPF8q/5CUviT/5kz9ZcCCeSCrPX/WqV6VuCjjiiCPijDPOiP33378lfwYAWiu50y45ETzzzDOjv79+jjFa5/2fvih2zLnZf+1++8XZZ59de/6BT38lds55beWalbOvASQcrwGWPsdqoFU++vnvxv1zupuvWL6i6XPEN/x7uhvohuN/Lc5+9NMibxyrAfat4/UZZ5wRvajnQvFsSX9f38L+iNntWtUa4LLLLqvND/6rX/1qdt3hhx8eRx99dC38vv3222st1JM7Ed/whjfE2972tlrl92tf+9oF7X9wcLD2yEq+XPiCAbC0OVZ3XvK7d+bvvK/YP7eLXZSrZf8eQEOO1wBLn2M10LRCZrFQaPq4Uoi+ZBavWZM5P+90rAbYN/T36LF64SXJ+4jly5enlsfGxhb0vux22f0sxn/8x3/E0572tNlAPLmzIplj/I477ojLL7+8FpjffPPNtdd///d/v7ZNMj94UlGetFwHAJpTTV1+SC5I7LrKUSqmb4ibqvTmXDkAAADsvSQUb3ofmfbpE+WppvcJACxOz4XiK1asSC2PjIws6H3Z7VauXNnUOJKw+0UvelGUy9MlaM961rPiG9/4Rjz60Y+u23bDhg3x2c9+Nl7+8pfPrvv7v//7uPjii5saAwAwfyjeV8iE4lUXJwAAAJjRglA8MqH41Jz+7ABAR/VcKH7AAQeklu+5554FvS+73bp165oax/ve977ZoD1pM/DBD35wj3OFv/vd70597nve856mxgAAeVetzn9Ro9Y+fQ6V4gAAAPlVrVZavs+kffpcY2WhOAB0S8+F4scff3xq+bbbblvQ+7LbnXDCCU2N45JLLpl9/uu//utx6KGH7vE9y5Yti6c//emzy9/73vdiYsIXJQBoWaX4nPZ3fZn26ZXqnAnGAQAAyLW5ncYWq1hIV4pPap8OAF3Tc6H4SSedlFpO5vBeiOx2J554YlPjSOYNn3HEEUcs+H1HHnnk7PMkEN+4cWNT4wCAPNvdnOL92UrxqkpxAACAvMqeP7ajUnyi7LwTALql50LxJIA+9thjZ5e//e1vL+h9c7c77rjj4vDDD29qHIODg7PPR0dHF/y+7NzmSfU4ALA42UsacwrF60LxsjnFAQAAcqvupuq5J5CLVMyE4pMV550A0C09F4onzjnnnNnnl112Wdx+++273T55fW4oPvf9izW3XfoVV1wR1fpJTRv60Y9+NPt8xYoVsXr16qbHAgD5tZtK8VI6FK8IxQEAAHIre/22Fe3TS4VspbipMgGgW3oyFD/vvPOiVJqer6VSqcTb3va23W7/1re+tbZdInlf8v5mnXHGGbPP77rrrvjMZz6zx/d85zvfiR/84Aezy0996lObHgcA5Nnu7vTvy1ycUCkOAABAa+cUz1aKa58OAN3Sk6H4CSecEC984Qtnlz/ykY/UHo186EMfigsuuGB2+dxzz43jjz++4ba33npr7WL6zCPZdj4vfvGLUxfeX/rSl8a3vvWt3c5p/rznPS+1LnkPANCeixoDfQOp1yohFAcAAMirdswpXixMF27NmNI+HQC6Jn2rWg9517veVWuJftNNN82G1F/60pfi+c9/fq21eVK9/elPfzq+/OUvp+YSf+c739mSz3/MYx4TL3/5y+P//t//W1vevn17nHnmmfE7v/M78axnPSuOOeaYKBaLtdbtF198cXz+85+Pyclddwr+3u/9Xpx99tktGQsA5FbS/i51c/+cUDwzp3ilWu7cuAAAAFhisp3Gmq8nq3Uom7PbibJKcQDolp4NxdetW1cLm5/xjGfELbfcUlt30UUX1R6NHH300bXtk/e1yvvf//6YmJiIf/mXf6ktJy3adzeGuXOaf+ITn2jZOAAgr+rbp+963l9Mfw1SKQ4AAEAr1dqnzzktLVeF4gDQLT3ZPn3Ghg0b4uqrr46/+Iu/iFWrVjXcZvXq1bXXk+2SSvFWSirBk/bsl1xySTztaU9LtVNv5ElPelJ87nOfiy984QsxODjY0rEAQD5lQvE5X30Gs+3TzSkOAACQW9Wk01iL5xTvK6Q7lE1qnw4AXdOzleIzVqxYUavYnmmnnswLvnHjxth///3jqKOOitNPP33BAXSyffbL0UI8/elPrz02bdoUP/7xj2st3bdu3VrbVxLKr1+/Pk455ZQ46KCDFvEnBADmk/2tPff+tIFS+uJEVaU4AABAbrV+RvGIUmZO8cmKSnEA6JaeD8VnDA0N1Vqpd9PatWtr4TgA0Cnz3+mfnVO8GuYUBwAAyK82VIpnzjvLOpQBQNf0dPt0ACDf6ivFd13UGOzLhOIFoTgAAEBe1bVP38NUmItpnz6lfToAdI1QHADoXZmLGsU5X32GMnOKa58OAACQX9U2NFDvK6YbtaoUB4DuEYoDAD1s/osa2TnFQ/t0AAAAWlkprn06ACwZQnEAIDd3+qfbp2cqxbVPBwAAyK1qW+YUz1SKa58OAF0jFAcAcmPuRY3hTCgeQnEAAID8qrY+FO/PVoqbtgsAukYoDgDkslJ8oC99caJQKEelUunY2AAAAFjCk281n4nXTdulfToAdI9QHADoYdWFV4pHxFh5siOjAgAAYGlrR/v0ilAcALpGKA4A5LJSfLh/sG770cmJjowLAACApab17dOzleJCcQDoHqE4AJAbc0Pxwb70HfuJ0UmV4gAAAHmUvam6FfqzleJRbvlnAAALIxQHAHpYdd4vPsN9jSrFxzswJgAAAJZ8p7EWVIoPltLTdlVCpTgAdItQHADoWdXq/F99GrZPnxKKAwAAkHQai5a3T69qnw4AXSMUBwB6WHZO8V3Pl/en79hPjE+5QAEAAJBH1bq7qoutD8W1TweArhGKAwA9bP72d4P96YsTCe3TAQAA8mr+m6oXS/t0AFg6hOIAQM/K3udfmHNVYyhzx35idGqiA6MCAAAgD3OK9/dlzjsLKsUBoFuE4gBAD5v/okaxWIxqpZR6fWJqsmMjAwAAYClpfSievRlb+3QA6B6hOACQ4zv906G4SnEAAIB8qptSvAWh+EC2UlwoDgBdIxQHAHI7J1yhmg7Fx4XiAAAAZKbfWqyhvvSc4tWCOcUBoFuE4gBAbkLxYt1Xn77U0kRZ+3QAAIB8akP79Ewobk5xAOgeoTgA0LP21P2uoH06AAAADabfaoXBzJzihUIlKpVKyz8HANgzoTgAkNs7/bPt0yemVIoDAADkUTYSb0H39Bjuz1SKJzdjTzrvBIBuEIoDADmaU7yw20rxiYqLEwAAAPnU+vbp2UrxxFhZhzIA6AahOACQnzv9s5Xi5hQHAACgQfv07E3VizHcP1i3bmRCKA4A3SAUBwB6VzV9UaOYrRQvZCrFheIAAAC0qFK8Ufv0kcnxpvcLAOw9oTgA0Lsy1zCyd/oXM5Xik+WpTowKAACAJX5Tdd0J5SIM9dW3Tx/XPh0AukIoDgD0rGrmokb2Tv9iZk7xyYpQHAAAII/q26c3v89lDdqnj07oUAYA3SAUBwB62O7nhCvWtU93xz4AAEA+ZW+qbv7SuUpxAFg6hOIAQG4VC+kLFFNVleIAAAC0plJ8oNQX1Wp6R6NTQnEA6AahOACQmzv9i4X0V59Stn26OcUBAAByqX5G8eZT8WKxGFFNn4dOTGmfDgDdIBQHAHLU/i6tVOhLLU9WXJwAAADIp2ws3irp806V4gDQHUJxACA/d/rXzSmevjgxVVEpDgAAkEd7On9crEI13aFsXCgOAF0hFAcAclQpnmmfng3FqyrFAQAAcqmaPX9sTSieTNw110TZeScAdINQHADoYZmLGplrGn3FTChuTnEAAICcak8onq0UH1MpDgBdIRQHAHKjmLmoka0UL1eF4gAAAHlUbdOc4oW6SnHnnQDQDUJxACA3FzWyc8KVCumLE1NCcQAAAFo5p3ikb8YeL6sUB4BuEIoDADmSvqjRV+xPLU9VzO0GAACQR9V2tU/PhOLmFAeA7hCKAwA9LH1Ro5i50z87p3ilWu7IqAAAAFhqdt9pbLGKmQ5lQnEA6A6hOADQw3Z/p382FDenOAAAAK2sFC+qFAeAJUEoDgDkRvZO//5s+3ShOAAAQC5VO1QpbtouAOgOoTgAkNuLGvXt04XiAAAAtE62UnxcpTgAdIVQHADIjWLsvlLcnOIAAAB51a5K8XQoPlVxMzYAdINQHADI0UWN2G0oXg537AMAAORRtdqeC+elTCg+qX06AHSFUBwAyFEonv7q019KX5yoqhQHAADIqWpbLp3XheLapwNAVwjFAYDelakMzza/68/OKR5CcQAAgHzafaexVoXi2qcDQHcIxQGA3M4JN1gaSC1Xqi5OAAAA5FG2TrxQd1t1iyrFqyrFAaAbhOIAQG7Ut09PzyleVSkOAACQU7u/qXqx+jIdylSKA0B3CMUBgJ5VzVzUyH7xGchcnBCKAwAA5FP2/LFVleJ9hfTN2FMVleIA0A1CcQAgt3f6D/Rl2qeHO/YBAABoYSheTIfiZdN2AUBXCMUBgNwoZL76DJTSleJRUCkOAABAclN1a/ZTKpZSy2Xt0wGgK4TiAEAPy7RPz1zUGDSnOAAAAA3OH1ulP1MpPqVSHAC6QigOAORGsZD+6jOYaZ8eQnEAAICcytxU3aJL59k5xStCcQDoCqE4AJCfOcUzc8INZCvFtU8HAADIpbo68Ra1T+/PTNtlTnEA6A6hOACQn1A8MyncsEpxAAAAFnBTdavap5eddwJAVwjFAYDcKGZC8YG+9MWJQrEclUqlw6MCAABgqd9U3apQvFKdbMl+AYC9IxQHAHJ7UWMw0z49MVHWyg4AACDv7dNbVSmenbbLnOIA0B1CcQAgN+rbpw/WbTM25a59AACA3KlWd9tpbLEGspXi2qcDQFcIxQGAHpa5qJG503+wv75SfGRyvO2jAgAAYInJZOCtap+enbZLpTgAdIdQHADI7UWNob6+ureoFAcAAKBV7dP7i+nzzqpKcQDoCqE4ANCzqtk5xTMXNZb117dPHxeKAwAA5E410z69ZZXipYHUciVUigNANwjFAYDcyF7UGOxLX5xIaJ8OAACQR+lQvFWGMu3TVYoDQHcIxQGA3M4pnr04kVApDgAAQKtkb8auFpxzAkA3CMUBgB6WaX9XTIfiww0qxcfLLlAAAABE3m+qLrTm0vlwX3baLpXiANANQnEAIDeyc4oP9PVFtZpeNzo50eFRAQAA0H2Zm6rbVikuFAeAbhCKAwA5utO/wWWNaim1OKFSHAAAIPI+o3ih0fljKyrFC1Mt2S8AsHeE4gBAbivFp6VDcXOKAwAA5FG2UrxF7dP705XihUI5KpVKS/YNACycUBwAyHWleCFTKT42pX06AABA5D0Ub1H/9GX92TnFI3ZOjrdm5wDAggnFAYDeVXcRY8+V4hNlrewAAADyrtiiWcWHG4TiI5NuxgaAThOKAwD5qRQv7rlSfKLs4gQAAEDetWpO8YaV4hNjLdk3ALBwQnEAINdzihfMKQ4AAJB71cxN1Y07jbUmFB9VKQ4AHScUBwDyPad4tn16RSgOAABAaywbGKhbN2JOcQDoOKE4ANCzCoXqHtvfFaIvtTxRFooDAADkz55vql6M5Q0rxYXiANBpQnEAIDeKDdrfFQvZOcWF4gAAAPmTuam6Re3TB0p9Ua2m9zU6pX06AHSaUBwA6EmVSqVu3UIqxSfLU20dFwAAAEtfo/PHxSgWixHV9M3YKsUBoPOE4gBAT6pU03f5z9f+rpgNxStCcQAAgLypZivFWxSK1/aVOe8cUykOAB0nFAcAchOKN7qoUd8+3cUJAACAvGs0/daiVdOhuEpxAOg8oTgA0JMq1Qbt0xvOKZ6+ODFVVSkOAACQP9lK8dbtuRDpm7HHypOt2zkAsCBCcQCgJ1UyFzTma59eyoTi5hQHAADIo2wo3rpL54VMpfiYSnEA6DihOADQkxoUijesFC9l7tifrLhjHwAAIO9aWCheN6f4uGm7AKDjhOIAQE+qRH0qXiw2CMWL/anlqYpKcQAAgNzJnC4WWtg/vS4UnxKKA0CnCcUBgJ5UqVYXVSk+VVUpDgAAkD/155Ctkp22S6U4AHSeUBwA6EnV6gLnFC+mL05MmVMcAAAg91o5p3gxUyk+UXYzNgB0mlAcAMhNKF5o8NUne8d+uSoUBwAAyJtqplK8lRfOi4X0tF0qxQGg84TiAEBPqjRofddoTvG+7JziQnEAAIAcqrZtTvHszdiTFZXiANBpQnEAoCdVKo0qxeuVCpk5xV2cAAAAyL1GncYWq5SpFJ9QKQ4AHScUBwByUyne6E7//kyleKVabuu4AAAAWIoy7dNbVyheXyluTnEA6DihOADQk6rVSt26YqH+q09f0ZziAAAApBUa9hpbnL5spXhFpTgAdJpQHADoSZXqwirFs6G4OcUBAADyqH1zivdlOpRNVZx3AkCnCcUBgJ7UYErxhu3v+osDmfe5OAEAAJB3rawUr2ufrlIcADpOKA4A9KRqg0rxYoOvPv2ZSnFzigMAAORR+yrFszdjT1XMKQ4AnSYUBwB6UjUqi2qfXg4XJwAAAGid/mz7dB3KAKDjhOIAQE+qNOifXmwQig+U0hcnqirFAQAAcqi6x/PHxerPnHeqFAeAzhOKAwA9qZK5oJFodE1jIHPHfiWE4gAAAHnX2vbp6fPOclUoDgCdJhQHAHIzp3ih0Zzipeyc4trYAQAA5E/75hQfKKXnFBeKA0DnCcUBgJ5UqS6yfbpKcQAAgPzJnC4WsytaWinuZmwA6DShOADQk4TiAAAALFS1jZXig5nzTqE4AHSeUBwA6EnVqNSta3RRoz9zcaISLk4AAADkXSHa1z7deScAdJ5QHADoSQ0KxRu2vxvsS1+ciIJKcQAAgPxpZ6V4JhQ3pzgAdJxQHADIT/v0YoNQvNiXWtY+HQAAgFbOKZ69GbuqUhwAOk4oDgD0pGrDOcVLdesG+tLt00MoDgAAkEOZSvEGN1W3rFLceScAdFy6NKpH3XXXXfGJT3wiLrroorj11lvjwQcfjHXr1sVRRx0Vz3rWs+KP//iP47DDDuvYWL7whS/El7/85bj55pvj3nvvjcnJyVi7dm0cd9xx8cQnPjFOO+20eMpTnhKrVq3qyJgAoBdVGswp3uiaxlDm4kRV+3QAAABaaKiuUlz7dADotJ4Pxf/5n/85/uqv/ip27tyZWn/33XfXHt///vfj7W9/e7z3ve+Nl770pW0bx8jISO1z3ve+98XExETd60k4njy++93vxnve8554/etfX9seAFicSqM5xRvMCTeoUhwAAIBspXgL26fXheJuxgaAjuvpUPytb31rvOlNb0qt27BhQxx66KFx5513xk033VRbt2PHjnjZy14WDzzwQLzhDW9o+TiSyvSnPe1p8bOf/Sy1/thjj41DDjkkSqVS7bNvuOGGWtU4ANCe9umFQnGPFycKxXJUKpUoFs0yAwAAkFeNbqperKH+wcwac4oDQKf1bCh+4YUXpgLxE088Mf793/89HvOYx8yu+/GPfxwveMEL4rrrrqstv/GNb4xHPOIRtZbqrbJly5Y4/fTT45prrqktL1u2LF796lfHi1/84rqW7UkFeVIp/vnPf17rdABoUqXaoH16LKRSPGKiPBVDxXRYDgAAQC/rZKW4UBwAOq0nQ/Gk2jppmT7j8MMPr4XN++23X2q7U045pbY+CcKTub4TyfvOPvvs6OtrzV/NK17xitlA/KCDDopvfOMbcfLJJzfcdmBgIJ761KfWHgBA6yvFiw0mFR/MzCmeGJuajKF+oTgAAEBuFNpYKZ4JxUP7dADouJ7sC/qZz3wmbrzxxtnlv/3bv60LxGesXbu29vqMpIV58v5WSALwj3/847XnhUKhVgE+XyAOALRWgynFG1aKDw/UV4qPTI63aVQAAADsG2eRrQvFl2XapxcKlZiYUi0OAJ3Uk6H4Zz/72dnnyfzhz3nOc3a7/TnnnFOb23vG5z73uZaM473vfe/s8+c///lx6qmntmS/AMCeVSqN5hRfeKU4AAAA+dWo09hiDWcrxd2MDQAd13Oh+OjoaHz961+fXT7rrLP22Ao9eT3ZbsbXvva1GBsba2ocN998c20/M170ohc1tT8AYO9Uo7KgUDx7x35iZMLFCQAAgHxp35ziywYanHcKxQGgo3ouFL/uuutifHzXF4onP/nJC3rf3O2SQDzZTzOSQHxmLtNkrvDTTjutqf0BAHun0mhO8Qah+IoGFyd2CsUBAAByrZVzig/31Z93jk5OtGz/AEAOQ/Frrrkmtbxhw4YFvS+73bXXXtvUOH7wgx/MPj/xxBOjv396vtKvfvWr8Qd/8AdxzDHHxNDQUG2u8+T1l7zkJakKdwCgOdUGs4oXG3z1WT4wVLduZKq5jjEAAADs2xp1Glus5Y1uxp503gkAnbT7vuL7oFtvvTW1fOSRRy7ofevXr08t33LLLU2N46c//WlqXvP7778/zj333Lj44otT2yVV7Vu2bKlVpn/4wx+O3/iN34hPfepTcdhhhzX1+QCQdw3nFG9wO+BAX19Uq8UoFHa1W98x7uIEAABAnhQK9eeQrdJo2q7Ricm2fR4AkINQfNu2banlNWvWLOh9q1evTi1v3769qXE8+OCDqeWnP/3p8bOf/Wx2DvNHPOIRtc+855574pe//OXsdpdffnmccsop8f3vfz+OPvroPX5OEqrPbRc/8+efnJysPQBYemaOz47T7TU5NVW3rjw5FZONGuVU+yMKu36f7hgf8e8DOF4D7AMcq4F2qZbLLTu29Dc4D902tiM3xy7HaoB9w2SPH697LhTfsWNHanl4eHhB78tu12wonlR/z0haps941ateFW94wxtqbdNn3HzzzfGKV7wivvSlL9WW77333jjnnHPiiiuuqAXou/OOd7wj3vKWt9Stv/TSS2PZsmVN/RkAaC/TZrTXdTsfqFv3tUu+Fn3FRqF4KbV45c9+Estu2/W7HMg3x2uApc+xGmh1p7Err7wqxq+/q2Wfke1Q9l9X/DBGrrs98sSxGmDfcOmll0Yv6rlQPHv3wp5C5fm2a/YuiLGx+rar73rXu+I1r3lN3fpkfvEvfvGL8bznPS++8IUvzLZf/+QnPxkvfOELd/s5r3vd62pB+9xK8SOOOCLOOOOM2H///Zv6MwDQHsnvmORE8Mwzz4z+/v5uD6dnLbvlF/HJ/0qvO/u3fiv6SukAPPHGf39Pagbyo3/t2Dj7lLPaP0hgSXO8Blj6HKuBVpgql+N//8cbU+sed8rj4oxjTm7ZZ7z+E2+JmBOKH3/ySXH2iU+IPHCsBti3jtdnnHFG9KKeC8WXL19eF04vpGI6G2Jn97OYccxt5f7whz88Xv3qV8+7fbFYjA9+8IPxla98ZXYsyRzjewrFBwcHa4+s5MuFLxgAS5tjdXuVGoTfgwMDtd+5WYVqXyoUHy9P+rcBZjleAyx9jtVAM6qFQt26/v6+lh5XkvPOiF2FWJNRzt1xy7EaYN/Q36PH6gb9Q/dtK1asSC2PjIws6H3Z7VauXNnUOLLvf8ELXhCFBl+u5jrggAPit3/7t2eXf/SjHy14/ABAWqVa3/6uUSBeWx/pL3pj5V3ziwMAANDbKtVdFdwzCrH7a7l7L33j9vjkRIv3DwDkKhRPguW57rnnngW9L7vdunXrWjqOU045ZUHve+xjH5tqU3DHHXc0NQ4AyKtqg1B8PoVCOhQfnRSKAwAA5EUl1TtsWnEPBU6LqxTfZXTKeScAdFLPheLHH398avm2225b0Puy251wwglNjePEE09MLS90fu9sGL9p06amxgEAeZW9qFGtzn9Bo5SpFB8vu2MfAAAgLxoUire8UryQmcl0fGpXK3UAoP16LhQ/6aSTUstXXXXVgt6X3S4bau+tZA7xucbHF3bnX3Zu8+Hh4abGAQB5tTeV4sVC+uLEmDv2AQAAcqMS9al4sdjaULyYCcVN2wUAndVzofgRRxwRxx577Ozyt7/97QW9b+52xx13XBx++OFNjeOpT31qavnmm29e0Ptuuumm1PLBBx/c1DgAIK/q5xSf/4JGX2EgtTxRUSkOAACQZ+2uFJ8oqxQHgE7quVA8cc4558w+v+yyy+L222/f7fbJ63ND8bnvX6zHP/7xsX79+tnliy++eEHvu+SSS2afH3300UJxAFikaoM54eZTyswpPq5SHAAAIMc3VbeeDmUA0F09GYqfd955USqVas8rlUq87W1v2+32b33rW2vbJZL3Je9vhZe85CWzzz/zmc/EDTfcsNvtP/vZz8Yvf/nL2eXnPve5LRkHAORRpaJSHAAAgMVNv1UstLp9evpm7InyVEv3DwDkMBQ/4YQT4oUvfOHs8kc+8pHao5EPfehDccEFF8wun3vuuXH88cc33PbWW2+NQqEw+0i23Z1XvvKVtXbuM3OF/+7v/m7ceeedDbf9/ve/Hy996Utnl5cvXx5/9Vd/tYc/KQAwv8xFjd3c+N9XzF6cEIoDAADkORQvtPjSeSlTKT5uTnEA6Kj0b+Ie8q53vavWEn1mju4Xv/jF8aUvfSme//znx6GHHhp33XVXfPrTn44vf/nLqbnE3/nOd7ZsDMPDw/Fv//ZvcdZZZ8X4+Hhce+21ceKJJ9YqyE877bRYs2ZN3HPPPbUxfOpTn4pyuTz73n/913+Ngw46qGVjAYC82Zs5xfuLAxG7fg3HZMXcbgAAAHlRaXAXdbFYaGso7rwTADqrZ0PxdevW1ebxfsYznhG33HJLbd1FF11UezSSzN+dbJ+8r5VOP/30Wvj+ghe8IHbs2BHbt2+P973vfbVHI4ODg/HBD34wnve857V0HACQN+Xq9NQoC1ELxeeYrLhjHwAAIL/Tb+3uturFKRbSHcomy0JxAOiknmyfPmPDhg1x9dVXx1/8xV/EqlWrGm6zevXq2uvJdkmleDs85znPiV/84hfxe7/3e7XQu5FkLvNkDvErr7yyZXOaA0CeVevu9J//ksZAXSju4gQAAECeK8WT6TNbqS8Tik9UTNsFAJ3Us5XiM1asWBHvf//7Z9upJ/OCb9y4Mfbff/846qijapXc8wXVWcn2jeaXWYj169fH5z73udi2bVttHMnc4ps3b66F8slrT3nKU2rPAYDWqDa4038+/aX0xYkpFycAAAByo9qg01ix0Np6sr6kffqc01Q3YwNAZ/V8KD5jaGio1kq925KK9Wc+85ndHgYA5PBO//nv8h8spW+Qm6q6OAEAAJAXlWoHKsWL/cmJ6izt0wGgs3q6fToAkF/13V120z69lG6fLhQHAADIj0aNxootnlS8ForP4bwTADpLKA4A5GRO8VhwpXi5qn06AABAnhVbfOk8O6f4lPbpANBRQnEAIBeV4oXdtk9PV4qX3bEPAACQ405jrddfyobiU23/TABgF6E4AJCPOcV3c41juC9dKV4JFycAAADyojp3su82zSneX9c+XYcyAOgkoTgAEHmfU3ywL10pXlEpDgAAkBuVBpOKF1scimen7TKnOAB0llAcAIi8t78b7htKvzdcnAAAAMhtp7FapXhrP2Moc945VRlv7QcAALslFAcAelJlLyrFh/rTd+xXC9qnAwAA5Pmm6kKLL50v6x9OLU9VheIA0ElCcQAg8l4pviwzp7hKcQAAgDzfVN369unLM6F4OcwpDgCdJBQHAHpSta793fwXNIZVigMAAORWV0JxleIA0FFCcQAgJ5Xi81/QWD6QntsthOIAAAC5UY1K3bpCq0PxgXQoXi2oFAeAThKKAwA5qRSf37JMpXihUImJKcE4AABAHjSafau4mxurF2PlwLL0Z2qfDgAdJRQHAHpSpe6ixvwXNFZk7thP7JwYa/2gAAAA2CcUi60NxVcNLk8tVwuTLd0/ALB7QnEAIDft7xZaKZ7YPjHa4hEBAACwr8wp3mqrhtKV4oXilA5lANBBQnEAIPI+p/jKwaEGleLjbRgVAAAAS//8MTmDbO2l81WD6VA8sXVspKWfAQDMTygOAPSkbJ14YTeh+PKB+lB8h/bpAAAAuVBp0GmsUGht+/Q1Q+n26YktYztb+hkAwPyE4gBAT6pWF94+fXmD9unmFAcAAMiHSoPu6S2eUjzWDNeH4luF4gDQMUJxACDy3j59qH8gqtX016Kdk0JxAACAvLZPL7a4Unx1g/bp28a1TweAThGKAwA9qe6SRoM7/9Ov96UWR80pDgAAkAuVBp3GCoXWXjrvK5WiWkmfdwrFAaBzhOIAQE7ap+/+Lv9CJhRXKQ4AAJDjSvE9nEMuRqE6kFreLhQHgI4RigMAPan+msYeQvHoTy2PTakUBwAAyINGjcWKrZ5UvHbemQnFJ4TiANApQnEAoCdVMpc1CntZKT4yKRQHAADIg0qlM5XixUyl+A6V4gDQMUJxACA37e/2rlJ8osUjAgAAYF9RKLQhFM9Uiu+cHG35ZwAAjQnFAYCchOK7v6BRjHSl+NiUOcUBAADyoBqVjnxOqTCYWhaKA0DnCMUBgJ5Ujb0MxQvpSvFRc4oDAADkQqVBp7F2VIr3FdKV4iNCcQDoGKE4AJCbixq7U8qE4uNl7dMBAADyeVN1cuN0G0LxYrpSfKysQxkAdIpQHADoUemLGoU9VIqXMnfsj5dVigMAAORBpdIgFG/DpfOB4lBqeWxKpTgAdIpQHADIyZziu1cqpOcUn1ApDgAAkNtK8UIbrpwPZCvFTdsFAB0jFAcAelJlL+cU71cpDgAAkEuNpt8q7uEcshWh+HhF+3QA6BShOADQk+qvaez+gkZfZk7xyfJk6wcFAADAPjKneOsvnQ/2pdunT7gZGwA6RigOAPSkalRSy3u6x7+/lK4Un6honw4AAJDX6beKhdZXig+VhlPLkyrFAaBjhOIAQE4uauxlpbhQHAAAIL/t09sQig9nKsUnqyrFAaBThOIAQE+qv6Sx+wsaA6X03G5CcQAAgPwqFlt/6Xy4P10pPiUUB4COEYoDAD2pUk23T9+T/mK6ffpUxZziAAAAeW2f3g7LMpXiU1U3YwNApwjFAYCc2H2l+GBmTnEXJwAAAPKh0qDXWDssz1SKV1SKA0DHCMUBgFzc6V/Yw3xwA5lQvFxVKQ4AAJDH88dqtfXziSeWDyxLLZfDzdgA0ClCcQCgJ1Wzd/rv4cb/wcyc4irFAQAA8qHSofbpKwfSleLVgvNOAOgUoTgAkI854fZQKT7Yl64Ur1Sn2jEsAAAAlvpN1XuYfmuxVmQqxasFHcoAoFOE4gBALu7039MljaFMpXg5XJwAAADIg0qlQ5Xig+lQPAoTUalUOvLZAJB3QnEAoCft7Z3+Q5lK8ao5xQEAAHKiM5Xiq4fSoXihUI3tE2Nt+SwAIE0oDgDko336Hi5qDPcNpZYrKsUBAADyOad4mwrHVw8ur1u3dWxnez4MAEgRigMAuagU32P79P5MpXjBnOIAAAB50Kk5xdcMr6hbt0UoDgAdIRQHAHpT3Z39u7+osbx/OPP2idaPCQAAgNzab6hBpfjoSFfGAgB5IxQHAHpSJSp7FYqvHV6ZWq4Wx6NSye4DAACAnm+f3qZK8eH+/qhW05fkt00IxQGgE/pavcMXvehFLdtXoVCICy64oGX7AwDyo+6axh6sGUq3sSsUKrFzcjxWDqYryAEAAOj19untUSwWI6r9EYXx2XXbx4XiALBPhuIf+9jHamF2qwjFAYDWzCm+++8n65avqlv3wM5tQnEAAIAeV610JhRPFKoDETE3FB/t2GcDQJ61pX16tVrd60ej9wEAtO5O/z2E4svqQ/GNI9tbPCoAAACWmspenj82o1gLxXfZoX06AOybleIvfOELF7xtuVyOzZs3x89//vO4/fbba+uSKvMzzzwzDjnkkFYPDQDIkewNdnu6pLHf8PKoVgtRKOx636ZRoTgAAECvqy/QamMoHgNRmbO8c1KlOADsk6H4Rz/60UW978orr4zXve518Y1vfCOuueaaeMc73hGPfvSjWz08ACAn9rZSvK9Uikju2J8zt9tmoTgAAEDP69Sc4olSYSCm5iyPTAjFAWCfbZ++GI997GPja1/7Wrz4xS+Ou+66K84+++y47777uj0sAGAfVT8Ty57v9C9UB1PLm8d2tHZQAAAA7AOdxtpXKV4qpM87d04JxQEgV6H4jH/8x3+MY489Nu6///54+ctf3u3hAAD7qEqqIV0yRcue31OqDqWWtwvFAQAA8jeneBsLx/syofioUBwA8hmK9/X1xYte9KLa3XkXXXSRanEAoGNzwmXv2N86LhQHAADodZ2cU3ygmKkUn9zZts8CAJZwKJ54zGMeU/tZLpfjO9/5TreHAwD0xEWNPesrDKeWt0+4OAEAAEDrDJdWpJZ3TGzv2lgAIE+WZCi+bNmy2ed33nlnV8cCAOybqrH3c8INZELxnZMjLR8XAAAA+a0UX9G/KrW8c0ooDgC5DcVvvvnm2edJtTgAQCcuagyU0qH4iDZ2AAAAPa+yiE5ji7VqMB2Kj5ZN2wUAuQ3FL7jggtnnhx12WFfHAgDkp1J8KBuKTwnFAQAAet1ipt9arDWDq1PL4xWhOADkLhQfGRmJl7zkJfHd7363tlwoFOKMM87o9rAAgJxc1Bgq7ZrCJTFWHm3hiAAAANgXbqpuZ/v0tcPpUHyq6mZsAOiEvlbv8N/+7d/2avvJycnYtGlTXH311fHVr341tmzZMhuI//7v/34cdNBBrR4iAJBDC6kUX9a3PLU8LhQHAADoeZ2cU/yAZWtTy+XCSNs+CwBoYyh+7rnn1gLtxX75mHnvcccdF+9///tbPDoAIC8Wc6f/8oF0pfhERSgOAACQv/PH9jlw+Zr0ZxdHolKpRLG4pJq6AkDPadtv2iTg3tvHzPue+9znxuWXXx4HHnhgu4YHAOTtosYCbtpb0Z+uFJ+sCsUBAAB6XaUuE29fpfjBK9KV4oVCJTaOmFccAPa5SvEjjzxyryrFBwYGYtWqVbF+/fo45ZRTaoH4hg0bWj0sACBnsu3vFvLtZNXgitTyVHWsxaMCAABgqalGpWOfdeiqdCieuGv7pjhgxaqOjQEA8qjlofitt97a6l0CAHSkffrKgXSleDmE4gAAAL2uk3OKH7JyTVSrhSgUdn3m3ds2xqMOOaptnwkAtLF9OgBAN2WvaSzkksaaoXSleLUgFAcAAMibQhtD8b5SKQqVodS6+3dsbtvnAQDThOIAQE7a3+35osZ+wyvT+yhMtHhUAAAALDWda54+rVhdllp+YGRLh0cAAPkjFAcAclIpvudQfG0mFC8Up2JkcrzVQwMAAGAJqVY7G4v3FdJdyjaPbu3o5wNAHgnFAYAelU3F9z4UTzy4c1srBwUAAECO5xRPDBaWp5Y3jasUB4B2E4oDAD2pmg3FF2Dd8vpQfOPIjhaNCAAAgKWo7uxx708n98pQKV0pvm1cpTgAtJtQHADIxZ3+i2mfntg4olIcAAAgX+3T21spvqwvfe65fXJ7Wz8PABCKAwC5qRTf80WNgb6+qFb6U+s2jbo4AQAA0Mvquqe3ORRf2b8qtTwypUMZALSbUBwAyMVFjeICL2oUqkOp5a1jO1s5LAAAAJaYSrv7pWesGkyH4qNTbsYGgHYTigMAPWkxleKJYiYU3zLm4gQAAEAvW8z0W81YM7g6tTxRVSkOAO0mFAcA8nFRY4HXNEoxmFreNu7iBAAAQJ7OH9tt/+F0KD5ZHeno5wNAHgnFAYAetbhK8b7CcGp5+6T26QAAAPnS3krxA5bvl1quFJx3AkC7CcUBgFy0T19o+7uBYrp9+s4Jd+wDAAD0ssVOv7VYB65Ih+LV4mhMlctt/UwAyDuhOADQkxZ7UWOguCy1vFOlOAAAQE+rdLh9+sGZULxQqMb9O7d2dAwAkDdCcQCgJ2WvaRQWOKn4YCndPn1kSigOAADQ2zobih++el3duru3beroGAAgb4TiAECPqizqXcOldKX4eHm0ReMBAABgKapWFzf91mIdsGxlVKvpS/P3bN/c1s8EgLwTigMA+agUX+BFjWX96VB8rGxOcQAAgF5W6XCleLFYjEIl3aXsgZ1CcQBoJ6E4AJCLOcUXG4qPV1SKAwAA9LL6KcXbWymeKFWXp5bv3H5/2z8TAPJMKA4A5CIUX+hFjf0G16SWxyvbWzgqAAAAlppq3fRb7Q/Fh4v7p5bv3H5P2z8TAPJMKA4A5KRSfGEOXrEutTxZ3dbCUQEAALD05xRvv1X96XPP+3be24FPBYD8EooDAL2prlB8YZc1Dlt1QGq5UtwRlUq2agAAAIBeUT+jePtj8QOHD04tb5rQPh0A2kkoDgD0pMXOKb5+9UHp9xUnY+PIjpaODQAAgKWs/aH4oSsOSS3vLG9s+2cCQJ4JxQGAnMwpvjDHrE2H4ombN93XghEBAACwFFWqne8Odsx+h6WWJ2NTx8cAAHkiFAcAcjIn3MLu9N9/2YqoVvpT627bKhQHAACgdX5t3RHpFaWdsVmXMgBoG6E4AJALhQXOKV4sFqNYWZlad9e2B9o0KgAAAJbaTdWdaJ9+8oHr69b94r7b2/65AJBXQnEAoCc1c1FjoJAOxe/d8WCLRgUAAMBSn35roTdVN+OAFasiysOpdb/aeEfbPxcA8kooDgDk46LGXrx3qLg6tfzg6MYWjQoAAIAlf1N19h7rNumrrk0t37T5zs58MADkkFAcAOhJzdzpv7wvHYpvGtvUsnEBAACwtFSyoXgHKsUTy0v7p5bv2n5vRz4XAPJIKA4A5MTCL2qsHtwvtbxtcnMbxgMAAMC+3mmsGfsNHphavn9UKA4A7SIUBwB6UrVaSS0X9uKyxv5D6bv1R6a2tmxcAAAALPH26R2KxQ8cPji1vHXigY58LgDkkVAcACDjwGXpUHyssq1rYwEAAKCzleKdCsWPXHVoanmksrEjnwsAeSQUBwB6UjNzih+88oDU8lRBKA4AAJAXnWqffsx+h6WWp4qbolJJdz0DAFpDKA4A5GROuIVf1jg8E4pXiztjqlxu2dgAAABYQqrdicWPP+DI9KcWJ+LObarFAaAdhOIAQC7mhNubUPzo/Q5Kv7dQiTu3ujABAADQiyrRnersEw84IqrV9CX67912bVfGAgC9TigOALCHUDxxy5b7ujIWAAAA2qvapUrx5YOD0V8+OLXuh3f/oiOfDQB5IxQHAHIyp3hxry5MRHk4te72Lfe3bGwAAAAs3fPHTlo3cHRq+YbN13dtLADQy4TiAECPau6iRrG6IrV89/YHmxwPAAAA+8RN1R2qFE8ct+bXUsv3jd3csc8GgDwRigMAPamZOcUTg4VVqeX7RoTiAAAAeTh/7FT79MQph5yUWh4r3BVjkxMd+3wAyAuhOADQk+ovaezdRY3h0urU8oNCcQAAgJxUinfOGcc8Kv3Zxan4/u2/7OAIACAfchGK33XXXfGud70rnvzkJ8dhhx0Wg4ODtZ/JcrI+eb0brrnmmtpYCoXC7OPcc8/tylgAoPdk5xTfu8saawbWpZbvH723JaMCAABgaakrFO9gLH7M2oOiUE7flP3d26/u2OcDQF70fCj+z//8z/Gwhz0szj///Pj+978fd999d0xMTNR+JsvJ+uOPPz4+9KEPdXRc5XI5zjvvvNpYAIBOtL/bO4evPCK1vHniniZHBAAAwNJUySx3slY8YlVxfWr5Fw9e19HPB4A86OlQ/K1vfWv86Z/+aezcuXN23YYNG+K0006LY489dnbdjh074mUve1m8/e1v79jY3vOe98QVV1zRsc8DgMh7+7u9rBR/2NqjU8tjcV9LxgUAAMDStpenj007csWG1PKdO27q7AAAIAd6NhS/8MIL401vetPs8oknnhhXXnll/OpXv4rLLrssbrzxxloofcIJJ8xu88Y3vjEuuuiito/tuuuuize/+c215yeddFIceuihbf9MAMi7vZ1T/BEH77qBrqa0I+7dvrm1gwIAAGDJ1Yl3ulL84Qfsukad2F69JabK5Y6OAQB6XU+G4pOTk/FXf/VXs8uHH354fPe7343HPOYxqe1OOeWU2vpkfvEZyfumpqbaNrZKpRIvetGLYnx8PIrFYlxwwQXR39/fts8DgLyqVitNheKPPezYqFbT77nizhtaMjYAAAB6Z/qtZp157GPTK0ojceEvf9it4QBAT+rJUPwzn/lMrRJ8xt/+7d/Gfvvt13DbtWvX1l6fccMNN9Te3y7JZ/3gBz+oPf+Lv/iL+PVf//W2fRYA5Fm1yRv9Vw4OR7Gc/v5w9X27vl8AAADQG7odip9y+HFRmjoote6Lv/xm18YDAL2oJ0Pxz372s7PPk9bkz3nOc3a7/TnnnBOHHHLI7PLnPve5towrad3+v//3/649P+qoozo6hzkA5E9mTvFFtL9bXkxflLhpy21NjwoAAIClpdqC88dmHbvilNTydVtVigNAK/VcKD46Ohpf//rXZ5fPOuus6Ovr2+17kteT7WZ87Wtfi7Gxsba0TU/Gl/iXf/mXWL58eUs/AwBo7UWNdUO7plhJ3LXjjqbHBQAAwFKvFO98KH72sWeklsdLt8X1D9zd8XEAQK/quVD8uuuuq83XPePJT37ygt43d7skEE/200r/8A//EN/73vdqz88999w488wzW7p/AKD1ofgRK45MLW+ecEECAACg1yyFSvHnPfw3oloZTK37xM++1vFxAECv6rlQ/Jprrkktb9iwYUHvy2537bXXtmxMN910U7z+9a+vPT/ooIPife97X8v2DQAsTGER1zR+bf+jU8ujcX/rBgQAAMCS0O05xRMrB4djbfGk1LrL77q0a+MBgF6z+77i+6Bbb701tXzkkekKr/msX78+tXzLLbe07AtV0jZ9ZGSktvyBD3wg1q5dG62SVMXPrYzftm1b7efk5GTtAcDSM3N8dpxur2q1klne+7/zk9atj7hhzorS9rhj0wNx8Mo1LRolsJQ5XgMsfY7VQCtUMuePSa14N44rTzjoSXHxfVfNLm+s/jSuuuOmePjBC7vGvVQ5VgPsGyZ7/Hjdc6H4TCg8Y82ahV20Xr16dWp5+/btLRnPP/7jP8bll19ee/7sZz87fv/3fz9a6R3veEe85S1vqVt/6aWXxrJly1r6WQC01te//vVuD6GnjY6ORgztWt68eUt89atf3bt9lCejWi1EobCrauBfL/58PGrFoa0cKrDEOV4DLH2O1UAz7rv/voiBXcuTE5N7ff7YCidMro6vVvqjUJwOIwqFSrzu4r+PPzvoadELHKsB9g2XXtqbnUp6LhTfsWNHanl4eHhB78tu14pQPKk2P//882dD93/6p3+KVnvd614Xr3rVq1I3BRxxxBFxxhlnxP7779/yzwOgecmddsmJ4Jlnnhn9/f3dHk7P+vtPXRhzvxXsv3ZtnH322Xu9n7/5t3+Iat/m2eXKQcvj7NP2fj/AvsfxGmDpc6wGWuGrF98QN+467YuBgYFFnT+2whc++4O4beqbs8t3910Rpz717bFqaN8tgHKsBti3jtdnnHFG9KKeC8WzJf19fQv7I2a3a7Y1QNI2/X/8j/8RO3furC2/5z3viUMPbX1V2eDgYO2RlXy58AUDYGlzrG6zpLp7zrRwxUJxUX/fq0qHx9bYdXXkZw/+PPr7X9CqUQL7AMdrgKXPsRpoRqFQqFvu1jHlFb9+Xrzye7tC8SjtjHd879Px3rNeFvs6x2qAfUN/jx6ri9Fjli9fnloeGxtb0Puy22X3s7c+9KEPxbe+9a3a89NPP70WkAMAnZPMIZ6WvsixUL+25uGp5TtHrlv8oAAAAFhykgKnpeJpxz0yVlROSK275O6Pxe1bHujamACgF/RcKL5ixYrU8sjIyILel91u5cqVix7DbbfdFq95zWtm27J/+MMfrrvbEABot/RFjcX+Lj718MemlseKd8TmkfR0LQAAAOy7qpnzx8XeVN0q5518XnpFaWe8/OK/6dZwAKAn9FwofsABB6SW77nnngW9L7vdunXrFj2G1772tbNzkr/5zW+O4447btH7AgBac1GjsMiLGs88/glRre56b6FQiS9f/6OmxwcAAMDSkC0U73Z500se91uxNh6TWnfL+DfjX664uGtjAoB9Xc+F4scff3xd1fZCZLc74YR0i5q9ce+996YC8qQybXePuZ/98Y9/PPXa3//93y96HACQZ3X3+S/yqsYBK1bFQOXw1LrLb79y8QMDAABgSalGJZZWLB7x92e+OaqVXXO6FgrV+MDP3xTfv+2XXR0XAOyrei4UP+mkk1LLV1111YLel93uxBNPbOm4AIDuxuLFwuK/9hwxnL5Z7pdbrl70vgAAAFjqleLdD8UffejR8eT9/yi9sjQaf/qNP4sr77qpW8MCgH1Wz4XiRxxxRBx77LGzy9/+9rcX9L652yXtzg8/PF0RtjdWr14d+++//4IfxeKuf4bBwcHUa8mc5ABA99qnJx578KNTy1vKN0Slkq0kAAAAYN+UTcW7H4on/vG3Xxn7x2NT6yp9D8R5l5wbX7/hp10bFwDsi3ouFE+cc845s88vu+yyuP3223e7ffL63FB87vsX48ILL4wHH3xwwY8kyJ/x/Oc/P/XaS1/60qbGAgDMWPxFjd/a8IT0itLO+OZNqsUBAAB68abqpaKvVIrPPfcfom/qsNT6amlLvPK758Vf/ec/u2EbAPIcip933nlRKpVqz5MvBW9729t2u/1b3/rW2S8PyfuS9wMAvdX/rpkb/R976DFRmFqTWvfxqy9c/A4BAABYMqrZ88cl0D59xgErVsUnfufD0Td1SGp9oTgVl9z3j/GEjz03Lrz2h10bHwDsK3oyFD/hhBPihS984ezyRz7ykdqjkQ996ENxwQUXzC6fe+65cfzxxzfc9tZbb41CoTD7SLYFAPaV9umL/9qTTHWyYcWTU+t+seVyd+QDAAD0pKUTiidOOuiIuPC5n4zB8tF1r42Wbow3XPE/4tSP/VF86EdfjYmpqa6MEQCWup4MxRPvete7UnOLv/jFL45nP/vZ8elPf7rWKv1Tn/pUPPOZz4yXvexlqbnE3/nOd3ZpxABAa7WuUjzxRyc/K7Vc7rs/vnL9lc3tFAAAgCV4U/XSc+SaA+Kbf/SZOHbwrIavby38PP7vda+Nx/7bafHc/zi/Vj0+VS53fJwAsFT1RY9at25dXHzxxfGMZzwjbrnlltq6iy66qPZo5Oijj65tn7wPANj3ZWeEa7b93XNOeEK89Qf7R6Vv4+y6T/ziwnjmCY9rar8AAAB0V6Z7+pK1emhZfPH574n/+4PfiA9f877U+ems0rb41dhX4g1XfCXe8IPlsa7vxHjMgY+Ls459YjzlqBNjqH+gG0MHgK7r2VA8sWHDhrj66qvj9a9/fXzsYx+Lbdu21W2zevXqWqv1//N//k+sWLGiK+MEADpRKd5cKJ60UD9+1alx7ciuucSv2/7tGJkcj2X9g03tGwAAgKVUKb4Ua8V3+fMnPDP+5FG/GX95yQfihxv/X0RppPGGpZ3xYPWK+Np9yeOfovrdvhisHhYHDR4dR646Ko5dsz4eftAx8bjDN8T+y1Z2+o8BAB3V06F4Igm63//+99faqSdt05N5wTdu3Bj7779/HHXUUXH66afH4ODCLmQn21fbcNtgMiYAYGlXiide8IjfjfN/sCsUr5a2xNsv/UT8zdP/e9P7BgAAoDvqr/ku7VB8pmr8I89+bWwc+bN462Ufj+/ee3FMlG7f7XsKxamYiNvijqnb4o5NEd/bFBE3P/RieWUMxv6xrLRfrOpfG2uH1sVByw+IQ5YfEAeu2C8OWr5fHLJybRy2am3ts5MbxwFgX9LzofiMoaGhWit1ACAnkosahdaG4r+14THx5u8dG2Olm2bXfeWOf483TP6xanEAAICe6TQW+4ykwvv9Z/95Uj8e37rp6vjITz4f12y9vHFr9d0pbY/xSB63xubJiNsmI2J7402rlVIUqsuilDwKQ9FXGIz+5FEcjIHicAyWhmKoNP1zWf9wDJcGY+ODG+Onl2+N4f6hGOgbiMG+gRguDdTauQ/17XoM9w3G8EB/7efygcHaufZAX18MFPuir1Rqyd8ZAPmUm1AcAMib1nd3Se6Ef9HJL4l/uu61s+uSCw1vv+wT8TdnqhYHAADohfbp+0KleCNPPfYRtUfiijtvjP/v2m/HFff9KO6buDaqfVta9jmFYrmWmJdrj4iJmReSv8byQ4+svoif3tn8Z1erxYhq8u+TVKoXozDneW05+VlNfhZml2vrkp+FXcvTj2SbwkM3QRR2LT/07197X2HX0uzPwnSV/K7tp7et20/tvXP3O33Hxez/1d7w0Kt1d2Kklxdyo392H4W93MfMn2u32+xhnwuZum6vx1m3WP8ZS33KA6b5d9o3Oqf84eAp0auE4gBAPuaEa9Gt/i895ay44BcfjPHSrulPvnTHR+K8B8+ODesOaclnAAAA0Dn72pziC/G4w4+rPSKmb+C+ddP98fWbfhI/vufncdu2W2Lz5D0xWr0/qqWtsS8pFCoPhaTl3d4e3/rb5Bcg+6FdGQRAkwaF4gAA+7RWXdRIqsXPPfEl8aHr/3rXytKO+O9feW1c9icfM68aAADAPqZuSvEeCMWzjlp7YLx47TPixZGeYnTzyI648u6b4uf33Rx3br837t/5YGwafzC2TW6KkfLmmKxuj2pxJKI41rWxA0ArCMUBgJ7UrkrxxJ89/rfjs9d/PjbHVbPrNsdP4s+/8v74p2e+smWfAwAAQCdU2nb+uNTtt2xFPO24R9YeuzM2ORH3bt8Sd2/fFPfu2BwP7NwcG0e3xo6Jkdg5ORojk6MxOjUSY+WxGJsajYnyWExUxmKiOh5Tyc+p0SiUkjP1qahGOaqF5OdURKE8+7NQUFoNQPsIxQGAHtW+9ndJNfgFv/3OOOeic2pV4jO+s+lf4w3fWBNvf9p5LfssAAAA6Lah/oFatXny2FuTk5Px1a9+Nc4+++zo7++fd7uJqanYOTEWOycnYnRyPEanxmNkYjwmylMxVSnHZKVc+zlVLsdUNf2zXHnoeeWh55XK9PPq9HK5Wnlo/UPrqpWoVCsPdQmo1G6LqFQqtRvskzl1Uz9T65KrDcn76tcn+5u+RX/OtslnzPm/6f+vPrTt9Py92Zv66+e4T0v2udvX97C/5DP35v2xh/fX721P2+/l5+/l57E07enfnaWhuofjw75OKA4A9KR2VoonkvnDz/u1V8dHb3pTav0X7/y7ePBLm+KffvuVWqkDAADsA7IZQC/MKb4vGujri4G+FbFftwcCkFOTD93E1KtcqQUAcqEd7e9edeo58YQ1L8h8TjW+t+lj8bRPvCRu3nRfyz8TAACA7lQwVkd3xuRNP2/7eACA1hOKAwA9qtqRLz0ffvar44ThZ9Wtf6D6w3j2F383XnPJv8TI5HibPh0AAICWdxprUCk+esmn41ePf2zc+NvPi7v+4DeiWtl9C2sAYGkRigMAPal+Cpz2fe35zO+9LR6/+r/Vv1DaERff+4F44r+dFX/xlQ/Evds3t20MAAAAtKpSPB2KJwH43W95e1Qmp9dv+9kDsfM/PtDBEQIAzRKKAwA9KjunePs+KZk7/ILfPT/OPfbNEeWhutcrfQ/GpQ/+Szzt80+N3/j4C+JN3/x43Ll1U/sGBAAAwMLVzSmeNvatz8fEpnRl+NYvfr794wIAWqavdbsCAFhK9tz+rtX+8tTnxlkbHh9/+p9viM1xVd3rheJUbI6fxP+78yfxhTv+LoYr6+PoFSfHEw87JX7nYU+IDesOafsYAQAA2EOleOau6q2fuqDuPSM3PFCrIC8U1Z0BwL5AKA4A9KS65nftLBWf46SDjojLX/jx+PAVl8SHfv5/Y7x0a8PtCoVyjJVujutGb47rbrwo/vXGiCiviBWFI+Lg4aNi/er1cczqw+OEA9bHow45Jg5Ysaoj4wcAAMib3c0pXhnZHlt/fFtd/fjUSCHGv/flGHrKszo2TgBg8YTiAECP6nyl+Fwvftwz4r8/9sz4+E++GR/7xSdiY/WnUSik2+3VKe2IHXFd3Dh+Xdx4f8Q374+IGx56rbws+qtrY6i4Kpb1rY6V/Wti7dDa2H94bRy0fP84eOX+sf/wqli3bPpxwPLVsXxwsBN/VAAAgH1atZq9rXqXHZ9+f1QmGp9P7vjSfwjFAWAfIRQHAHJ3p3+nJHONn/fYM2uP27c8EP/y4y/Ht+/8Zmyu/iIKxfLe7aw0EpORPCK2VyLuG4+I5LF1/rdUK31RqA5GsToUpcJw9MVglAoD0VcYiFKhP/qLA9FXHIiB4kD0l5KfgzFQ6o/B0mDtMdQ3vdxf7Iu+Yumhn33RXypFX6k/Boql2vJAqa/2Wn/yszS9PPNIlof6ks9Jti3WKvaTn8VCKYqFiNJD60uFh14rlKJULESxUJzeTitCAACgw+aeP+649Fvzbrf9ip/Hug6NCQBojlAcAMiFDnVPn9eRaw6Itz/tvIg4L+7bsTW+eO334jt3XBE3bvt57Iibo1BM4u7WSuYwj5iKSuyMpEa97hOSlXsoXl8KqtXkH++hx+zzRHH6YlXt/oeZ9TMh+vRy/c0QDy3P3jMx83phATdRLHTb9Ha7Xp27XeP3zLb5nxlfYb59dO8/6Lx98tRUOd727x/p6p+807pxE9HMJ9M5y0v7xf885cXx3JOe1O2hAMCSM3f6ranN2+fdbuyeiZi6+5boO/ToDo0MAFgsoTgA0KPSleLF2bC0+w5asTpe+viza4/ExNRU/PCOX8UP7rwmfv7A9XHnjltj29T9MR4PRpTmvwCTF4VCdde/515mZvM3QWzP++hBpYi97OsA+4SxiHjzj34Rv3nMZbFmeHm3hwMAS6x9+pw5xUcndvPOQoz/4JLoO+dlbRsbANAaQnEAoEdV95kCxIG+vnjK0SfWHlmbR3bEz+69Na594Na4a9v98cDoxtg8tjm2TWyJnVNbYrSyLSar26NS3BlRmHgoQAaABSiOxTdv/plqcQByLzv91lzl0d139Rq7+spYfk4bBgUAtJRQHADoSfX3+S/hVHw39lu2Ik4/5uTaY0+myuXYOLojHty5LTaObIsHR7bFltHtsXlse2wb3xHbJ0ZivDw+/ZiaiInKeEyWJ6d/ViZiqjoZUw/9LFcnolydjEpMRTUqUa2WoxrJo1J7RGH6+XQNbSWqhYd6sRcqUag9B2BfMFlOptoAgHzLhuJzzx7LY5XUmr7lEVM7d70+fsONnRgiANAkoTgA0KMyFzW6Pal4B/SVSrXW7MmjmyqVSowlYfvUVIyXp2o/J8qTUalWo5yE69WIcqUSU9UkbK9GuVKOcuWh15LovVKJcrVS+1l7PXlerUSlUo3K7PPKdDz/0PL0fiq15elHRKU63fR6JqKvViup1ojVuvUPjX/Ofzuz286+Z/pn8md5aIvU6zPvnXl5+saBdDvGmWfJOOfKfsbsPudZv9QqaNr8wV1RrkzFHXfeFUccflgUip2bgqGL/8Rd+zfu2n9bOXTx3R+KQnFXxVtyDAUAYt7zx8p4+rVlxx0Q2372wOzy+B0PdnJoAMAiCcUBgB6VvdO/90PxpaJYLMay4mAs6x/s9lCgKZOTk/HVr341zv7Ns6O/v7/bw4GW+M9//dfkv+7Z5eQmIwDIu5kbZXeZPn+sbN8c1Ur6XHLZKY+JbT+7ZHZ5/MHJqI6PRWFwqCNjBQAWp3PlDgAAHZStOSzmoFIcAPYs/fsw6bIBAETDm6rLD95d99qy33x2ajkJzSeu/m7HxgYALI5QHADoTdX8tU8HgL0OxR+aagIA8my+qVwqD9SH4gMnPTH6lqW3H7vy8raNDQBoDaE4ANCbMhm49ukA0CgUVykOANlQfOam6vLGe1Pri33VWpv0wUNWpNaPX/uLDowSAGiGUBwA6ElVleIAUKeQuQxQzvy+BIA8qjt/nGmfvumB1Pri4PT6oaOPSK0fv+XOto8RAGiOUBwA6FEu8gNAvfRNYpWK9ukAkDUTilc2P5haXxoq1X4Onnhyav3Y3ds7ODoAYDGE4gBALhQLvvYAQFTTvw8r2qcDwLzKW7aklkvL+ms/Bx/1xNT6qZ0R5Qfu6ujYAIC94+owANCjsu3vAICZyrcZ2qcDQP2c4jPK29KheHHZYO3nwKOeElFIv2f8qm+3cYQAQLOE4gBAjzKnOADsuX26SnEAqD9/nL5sXtm6LbW+tHyo9rO4bGUMrElfWh//+Y/bPkoAYPGE4gBAT8re51/wtQcA6kLxsvbpABDVeTqnlHeMpJZLK1fMPh88ZE3qtYkbftWm0QEAreDqMADQo9IX+RWKA0Cj9unlro0FAJbuTdXTvy/LO9OheHFuKH7UEanXxu+4t40jBACaJRQHAHKhaFZxAKi7DFBRKQ4A9e3THzp/rIyMp9aXVu+qDh88/sTUa+P37WzrCAGA5gjFAYCeVDWnOADssVK8Mk+7WADIc/v0mfPH8shkan1xbij+yCekXpvaGVG+/462jhMAWDyhOACQE0JxAMj+PlQpDgD1N1XPKI+lpxkp7bdu9vnAI06NKKTfN37lt9s0QgCgWUJxAKBHpS9OFFWKA0AUtE8HgD2aqRSvjKfPK0trD9i1zfDyGFhTSr0+fs1VHRohALC3hOIAQK7mhAOAPMtOJ1KuCMUBICs5f6xOTkRlMv17s7j2oNTy4KGrU8sTN/yqI+MDAPaeUBwAyAVzigNAQqU4AOypfXoSilcevLtuu9IBh6SWB486IrU8fvs9bRohANAsoTgAkI+LGkJxAKjrnFKtNp5DFQBypcHvw/IDd9WtK607LLU8ePxJqeXx+3a2YXAAQCsIxQGAXChqnw4AtVh8rqlquWsjAYCloi4SL0SUH8xUfRerUVi1NrVq8JFPTC1PjRSifP8dbRolANAMoTgAkIvLGsWirz0AUNA+HQAaaNA+fdMDqXWlgYhC5rxy4OFPiiik3zt+5bfbOE4AYLFcHQYAcnJRAwDItk+vaJ8OAA3nFC9vzoTiQ/WX0gvDy2Ngv1Jq3fgvftymUQIAzRCKAwC9KZOCm1McAOp/H1ZCpTgANAzFt2xKrSsO9zV87+Aha1LL4zfc0IYRAgDNEooDAD0qc1Gj4GsPANS1T68IxQEgK7mHrLJlS2pdaXig4baDRx+RWp64/d62jg0AWBxXhwGAXPClBwDMKQ4AjVTrphMpRmVsNL1maJ5Q/PiTUsvj9+9s+fgAgOa5PgwA5KP9nfbpANBgTnGhOADUdxqLqI5PpNf1z9M+/RFPSC1PjRSifN9tbRgjANAMoTgA0KOyc8L52gMAkZlOJHsTGQAwfRNZdXIyvW6gv+G2A494ckQx/ft0/KrL2zo+AGDvuToMAORCUaE4ANRViper5a6NBQCWbKexKERlIl0pXpwnFC8MLYvBNaXUuvGf/7gNowQAmiEUBwB6VP1FDQDIO+3TAaCR+s4p1YlMpXh/41A8MXDomtTy+A03tHBsAEArCMUBgFwwpzgAJL8PM+3Tq9qnA0D9r8NCfSg+ODDv+wePWZ9aHr/93lYODwBoAaE4ANCjVIoDQJZKcQBYwPljoRCVyan0usHBed89eMLDU8vj9420eHwAQLOE4gBAbi5qAEDeFTOXASoqxQGg4Zzi1UwoXtxNKD706CenlstjhZi6/VctHiUA0AyhOACQC0WhOADU3SSmUhwA6mcUT35dVifL6XWDQ/O+v/+kJ0ShlN7L+I+/1dIxAgDNEYoDAD1KpTgAZBUylwGqIRQHgEbTb9WH4vNXihf6B2Jw//7UuvFf/KTFYwQAmiEUBwByQSgOAMnvQ+3TAWCP7dOTOcWn0jeOFYeGd7uPwSPWpZbHb7yxhSMEAJolFAcAelT6okYxhOIAkFS+zVWppqvgAICHKsWnMkH5nkLxY49JLY/d8UBbxgYALI5QHADoTZkMXKU4ANS3T69onw4ADSSheGXBc4onBk9+dGp54oGJqE5NtWV0AMDe61vEe6Djrn/g7vjBHdfFjomRWt3foSvXxQkHHBEPW3doFIvu7QCgcfu7wm4q4wAgj7I3iVW1TweA5Bdi6sbq5NdltZz8jty1sjC0bLe7GHzsaRHxj7PLlalCTF7/4xg46QntGTMAsFeE4ixZm0d2xF9+7QNx1cZvRLnv/sYblYdjOA6Pg4bXx8P2e1g88YhHxG8e88hYM7y808MFYIlTKQ4AjdqnqxQHgOyc4olKpsi7uIfrjX1HnxSlwWqUx3f9rh2/6rtCcQBYIoTiLEkf/NFX44M/f2dU+zbv/r/S0miMxg1x68QNcet934hL7ot40xXF6C8fHAcMHhO/tub4+PXDHh5nHvfoOHjlfh38EwDQfeYUB4CsYqGUWlYpDgCJzPzhUYxqOb1FYQ+heKFYjMEDh2PkjrHZdePX/DRWtnagAMAiCcVZcv7ph1+Of7ru9VHoW1zFQqFQiam+u+Oe8t1xz8bvxrc3Rrz76oji1AGxX9+RcejyI2PDfsfGIw7eEE884vg4dNXalv8ZAFiCFzWKQnEAKGY6p5hTHACyZ48RhWp1r0PxxOCRB8XIHbfNLo/fvOs5ANBdQnGWlC9d96P4p2vfFIVi/YWZaqU/itXky2c1KsUdUShkvpnuQaXvgdgYD8TGnVfGz3dG/L87I+LHSQv2lTEY62Jl37pYN3RwHLL8kDh6zeHxa+uOiIcftD4OX7W/ecsBeoA5xQFguvJtrqr26QBQF4v3lZPe6elzyMLwij3uZXDDhojvzQnF79rUwjECAM0QirNkbB0bidd//9VR6JtIrR8uHxf/89H/M37/5FNjqH+gtm6qXI6f3nNLfP/2a+LqB34Zt2y7MTZO3BJTpfujUNjL9n+l7TEe22O8eks8OBrxy9GISx+MiBunX65WSlGsroz+WBlDxdWxvG9NrB7YL9YOrY01g6tjzdCqWDu8MvYfXh3rlq+Og1asiUNW7herBoeF6QBLqX26OcUBIAqZ34eN5lAFgLzJ/jYcmMpMKJ6cUy5bQCj+yMdHxDdml8c3laM6unNBVeYAQHsJxVky3vDNj0S1L3335IHFJ8Qlf/zP0VdKz3uXLJ9y+HG1R8SzZ9c/sGNbfOOmn8QP7vp5XL/purh/4uaYKN5da6m+WIViOaqxJSZqjztiWznintGISB67Ua2WolAZikJ1MIrRX3uUCgO1R1+hP/oKg9FfTB79MVAaioHS9PpSoRR9xVJtrr/k5/RyX+1RKhajv5D8LEV/qS9KhWLdRa25lR/Z/GfufLrZ902PefoUoJJcGnvobKD6UDvF5LWZE4RKpppk+rVqeh8N1s3uobqrTeOuz5l/++nF6e1nPnl2m3k+Jzs34uw2c16bu/9df85d79j99unPTG2fGvdDf6Nz/px1P+d7bc6fIfVny5yqza3u2e0+Zt839++Oztr1d79z58744H98reH/FmmRTEcRleIAkJwTpG/czX63B4BcSq6fzDll7J9IF+0kCsv2PDv44GNPj4i/mbPfQoz/9Dsx9MSzWjZUAGBxhOIsCVtGd8Zl930mYk72PVg+Or74/H+oC8R354AVq+IPH3la7TFj+/hofOumq+O/7rw6btpyS9w7cntsK98d5dKDe19Vvhdq7d1LO6MaOyOJZZLHZHajykOP+ptPgV43FLFl72aBYC/V3RjkBgQAiEIh0z7dnOIAUFcrPjDVKBTfc6V46cAjom95xNTOXevGf/Z9oTgALAFCcZaEv/7Gv9TamM/1l4/9y1g5ONz0vpN9PPvEX6895krC8ivuvCF+cd8tcfPW/7+9+4CzoyobP/7M3La9ZNN7r3QIIiAQuoioYMEKFv6+gK++YnlBRRT0FRBBxdeGCCgCorwiVUDpIC2hJoH0bNpmk8323Vtn/p8zm7275967ye7eMrf8vn7GvWfuzJknyXKmPHPO2Srbu7bL7t6d0hndJSFpEcvsyGrSHACQW9WBCrdDAADAdYkjp6gRngAAKHWJY+n5IpFR9RRXAhOrJLq+K14OrV6ZgQgBAEC6SIrDdZZlybPNf9N+G6utxVpv72xQyfIT5xzkLKl0h0KyetcW2dzeLNvad0lT125p7t0trcFW6Qi3SXe0VcJ2t0TtXokZvWIbvWKYdPkGgHzkjU6SY2cscjsMAABcZyb2FGf4dAAAkvgjyc/4jLLhzQsemDlZuteviZdDm7ZmNDYAADA6JMXhuvvffllsb6u27suHXyxuqwwEBs1bPjwqkd7U1So7u9pkV3e7tPS0S0e4R3ojIemJ9EpvNCjBaEiCsaCEoiEJWSEJx8ISjgUlYoUlZqv5y2MSs9Uc1DFn3u2+n2q95cz3p77vW9Rc14PHXh48d/Rgg0upe4GoupLn2lXlvnWDZiLXSgPr9s5R3l/93iGK9b33X1dfDKm22/ttfOW+9hu8p37sVHHpW+vb7SsubV1CXPp3yfEkxmVosSUfe6COge2T1yXPF598vOThMvv+1Awp7Yb29napra1lTvEcGF8xXi5598dHNB0HAAAlkxQf4h4BAIBS5ovow6cbHlsMUz+HDqVs4WKRfw1Kim/vyHh8AABg5EiKw3V3r35YK5vRcfLRA46VQqQS6XMCE2VOw0S3QwGQxyKRiDz00ENyxhlniM/nczscAABQQhJfyKOnOAAAyS+JeROGTzdG8I514GA1heO98XKkU8RqbRazfnz6gQIAgFEb3uttQBatavu3Vp5X/S4xh/nmJQAAAABg+MyExwD0FAcAQNn3nOIjSYr7D1smYuj1hV55PL3wAABA2sg8wlUvb10nUe92bd2HFpzmWjwAAAAAUMzMhJ7iasomAABKXVJP8ag+p7jpHf7UZ2ZVrfhr9cfuoTdeTjNCAACQLpLicNWtrz2gr4hVyUeWFObQ6QAAAACQ74zEOcVteooDAJDIF9GT4sYIkuJKYEqdVg6+83ZG4gIAAKNHUhyuenXXC1p5iv8w8XuZ6h4AAAAAcjN8esy1WAAAyB/77ilueEf2GD0wa7pWDjXuSCM2AACQCSTF4RrLsqTT3qCtO2H6ca7FAwAAAADFjp7iAAAkSzwdJvcUH2FSfPFBWjnU1DP64AAAQEaQFIdrXt66XsTTq607be6RrsUDAAAAAKU3pzhJcQAAEnuKe5LmFPeMqLbAIcdo5VjQkGjjmjTiAwAA6SIpDtc8vnGFviJWKQdPnOFWOAAAAABQ9DyG/lDfFsu1WAAAyNvh0xN7ivtGlhT3H/huMTx6naEVT6URHwAASBdJcbjm1eY3tHK1OUtMk19JAAAAAMgWI6GneNJ4sQAAQLzRmFY2fN4R7W/4/BJo8GnrQm8uz0hsAABgdMhAwjVbutZq5ZlVC1yLBQAAAABKgZk4pzg9xQEASJpMxBvRk+LmCJPiSmBKg1YOrV03qtgAAEBmkBSHKyzLki57k7bu0AkHuBYPAAAAAJRmUpye4gAAJM8pntBT3D+KpPjc2Vo5tKV5lLEBAIBMICkOV7y0da2Ip1dbd/Kcw12LBwAAAABKgZnwGMBm+HQAAJJeEjOTkuL6UOjDEVhyiFYO7QqLbTFCCwAAbiEpDlc8vnGFviJWJQdPnOFWOAAAAABQknOKM3w6AADJvFH9/Gj4/SOuI3DYcVrZihoS3bgy7dgAAMDokBSHK15v1i8Aa8yZYpr8OgIAAABANjGnOAAAqey7p7g5ip7i3tkHiOHR642sXj7K+AAAQLrIQsIVTT1btPLUyjmuxQIAAAAApcJjerQyc4oDAJBqTvHEnuKBEddoeL3iq9XPu+F1q0YZHwAASBdJcbiiI7pTK8+sYeh0AAAAAMg25hQHACBZ4tkwKSkeGPnw6Yp/bJVWDm/eOKp6AABA+kiKI+csy5KIuUtbt2jsLNfiAQAAAIBSYSbMKS4Mnw4AQIrh0xOT4mWjqtU/aZxWjmxrGlU9AAAgfSTFkXNrWraLYYa1dYdMnutaPAAAAABQunOK01McAID9JcXNwMiHT1d806Zr5fDO9lHVAwAA0kdSHDm3fPs6rWxbXjlg/DTX4gEAAACAUu0pTlIcAIBkZlQ/Pxpl5aOqxz9ngVaOtOkdhQAAQO6QFEfOrd6lz53jtcaK1+NxLR4AQOmxo1Fpu/7r0vT506X1mi9LbMcmt0MCAMCVnuIMnw4AQPKc4plKivsWHKKVYyFDYs1bRlUXAABID0lx5NzG9s1audozwbVYAAClqe2Gr8uO3z4orc9tlqZbHpO1J50uLZd/zu2wAABwYfh0kuIAAIidMHx6LEM9xRcdIWLodYVXvTSqugAAQHpIiiPnmnq2aeVxZVNciwUAUJo6H39GK9uWIc1/+beEX3vatZgAAMgFT2JSPCEJAABASdJnFxEzllAuqxhdteWV4qvUK4+seWtUdQEAgPSQFEfOtUd2aOVpNcwnDgDIrUhbb8r1PU/cn/NYAADIJY+pT13FnOIAACS/JGYkDp8eKBt13b6xei/z8MZ1o64LAACMHklx5FxIdmnlBQ0zXYsFAFB6bMuSSGfqoWJD77yd83gAAHB3TnGS4gAAJJ4PjVhyj+/R8k9o0MrhrdtHXRcAABg9kuLIqS1tLSKeHm3dwRPnuBYPAKD0WLu2iB1NGBtvr1CjPpoJAADFxjQSz4HMKQ4AQCIzk0nxqfrUkZGde0ZdFwAAGD2S4sip5dvXamXbNuWQibNdiwcAUHoi61cO+V1oZ3dOYwEAwO2e4gyfDgCAMnA+NCxbDEt/icysqBp1zb7Zc7VyZE9w1HUBAIDRIymOnFrZvEEre2L1UhkIuBYPAKD0RDe9M/R33SKx5i05jQcAgFzyMHw6AAApDJwPfQm9xNPtKe6bOV8rR7ptsSPhUdcHAABGh6Q4cmpLpz4sbbk5zrVYAAClKbJ10z6/Dy1/MmexAACQa/QUBwAg2eCzoS+a/L1RUT3qun1zD0o4mCHRzatHXR8AABgdkuLIqebuZq1c421wLRYAQGmK7Ni+z+9Db76Ss1gAAHA7Kc6c4gAA6FL1FDfTSIp7pswVw9RfQouuXzXq+gAAwOiQFEdOtYV3a+X6srGuxQIAKE3RZv1clCi0Zm3OYgEAwO3h0+kpDgBAwvDpGe4pbni94q3S5yiPNHLfCQBArpEUR051Rfdo5QkVE1yLBQBQmiIt7VrZ9OnJgFCjPtUHAADFxGPSUxwAgGQD94X+DCfFFV9dmVaObNmcVn0AAGDkSIojp0J2q1aeWk1SHACQW9G2oFaunK9P5RFq7s1xRAAA5I5pevQVBj3FAQAYLKmnuGGL4fOnV2dDjVaONDWlVR8AABi5kkiKb9u2Ta655ho55phjZMqUKRIIBJyfqqzWq++zZePGjfL73/9ePve5z8mRRx4p48aNE7/fL9XV1TJjxgw588wz5dprr5WmErgQsixLYqbeO29G3STX4gEAlB7bsiTSqfeIqzrm3Vo5FjQkunV9jiMDACA3PIY+fCvDpwMAoJ8PE5Piie+TjYZ3vD6FZHSXPpomAADIPq8UuV//+tfy9a9/Xbq7u7X127dvd5bnn39efvCDH8h1110nX/ziFzN23FtvvVVuuOEGeeONN1J+H4lEpKurSxobG+XBBx+U73znO/LVr35Vvv/970tZmT6cTrHY2tEihqlfVc4fO9W1eAAApcfatUXsmJ4MqDjpAyI3PSBiD6yPrHlVvFPnuBAhAAC5nVN88HCxAABAxB/Tz41GBp6g+yZNFpFV8XJkj/6sGgAAZF9R9xS/8sor5cILL9QS4vPmzZPjjz9e5swZeNCtktP/8R//4STHM+Wvf/1rUkJc9RBfsmSJnHDCCXL00UdLfX29liRXPcZPPfXUpAR+sVi9a2vSuvkN6oIQAIDciKx7K2GNLb75h4m3Uk+URza+ndO4AADIFZOkOAAAKQzdU9zw6PeLo+GbPlsrRzrCadcJAABGpmiT4n//+9/liiuuiJcXL14sy5cvlzVr1siTTz4p69atk5dfflkWLVoU3+byyy+X++67L6Nx1NXVOQl3dcz29nZ566235IknnpDnnntOWlpa5IEHHpDZswcuip555hm5+OKLpRhtaN2hr4hVSWUg4FY4AIASFN28Rit7ykWM8krx1enno0jjphxHBgCAW0lxfVoRAABK0z6S4t70k+LemQu0cqzXEKunM+16AQBAiSfFVa9rNWR6v6lTp8qzzz4rhx12mLbdEUcc4axX84v3U/tFowlXPqMwefJkuf76650h2n/1q185vdMTh0U3DEPe9773yUsvvSTz58+Pr7/tttvkzTfflGKzpV1PivukzrVYAAClKbJlo1b21fj7fo6t1bfbvj2ncQEAkCsek57iAADsiz9xTvEMJMV9cw9MWhddX3zPfwEAyGdFmRS/6667nJ7g/VRyevBQ5YONGTPG+b7f2rVrnf3T9dvf/taZI7y8vHy/2zY0NMhPf/rTpOHXi82Orp1aucJM/W8CAEC2RJuatLKvvqLv58Rx2vpI856cxgUAgGtzihskxQEAsAe9JOaN6d8Z3vQfoZtjp4jp08+5kQ0Dc4wDAIDsK8qk+N1336312P7Qhz60z+3PPvtsmTRpUrz8l7/8RXLtlFNO0RLoq1evlmKzO7hLK9f4GlyLBQBQmmKdXVrZU1Pp/PRNmaqtj+zRtwMAoFh4TM+QSQAAAJDcU9zw6ufO0TBMU7zVej3RxvVp1wsAAEo4Kd7b2yuPPfZYvHz66aeL1+vd5z7qe7Vdv0cffVSCwWBW40wVQ01NTbzc0dEhxaY93KKVG8r0XnkAAGSb1durlc29L6T5ZszV1kfa059KBQCAwhg+nTnFAQAYLGlOcV9mHqH76vQRRSPbtmSkXgAAUKJJcdXDOhQKxcvHHHPMsPYbvJ1KiOe6p3ZPT480NzfHy+PHj5di0x3Th6KdWDXBtVgAAKXJ6h24RlDMqsqU87tZYUNie/Sh1gEAKMrh0+kpDgCAdj70JQyfbvr23eFquHxja7VyZOfAs2AAAJB9RZcUX7lypVaeN2/esPZL3G7VqtzO6XLPPfeIbQ9cfB199NFSbCLSppWnVU90LRYAQGmygmGtbFZWOT998w9J2jbyzms5iwsAgFwhKQ4AQCoD50N/VD83GhlKinsn6h2EIrtaM1IvAAAYnsyc0fPIpk2btPL06dOHtd+MGTO08saNGyVXIpGIXH311fFyRUWFM8/5cKhe8YN7xvcPu67qVEu+CEejYpkdYgxaN71mfF7FCAC50t/20QbmXqw34e+8orLv3yFQIZ5yW2K9A2eq0No3xXPESbkPEkDeoL1GMbKthOHSDZvfcRQ02moA2R4+XXyejLQxnomTReTVeDna2lMybRdtNQAUhkiRt9dFlxRPnIu7rq5uWPvV1urD13R2dkqufOc739F6pn/9618f9vDpP/rRj+T73/9+0vonnnjCSa7ni23BDjEM/S3LHavWy0PrGSYIQOl67LHH3A6h5Czu1XuKN7bskcaHHnI+L6k0JdY7cK7a8uJzsr52fs5jBJB/aK9RTN7oSpwexJaH9p4LgUJGWw0gHbZYQybFe8LhjJwrJ3aHpGZQOdIZLblzMG01ABSGJ554QopR0SXFu7q6tHJ5efmw9kvcLldJ8bvvvlt+/OMfx8uHHnqofOtb3xr2/pdddplccskl2ksB06ZNk2XLlklDQ4Pki/vffllkxUDZtk05/6wPi9fjcTMsAHCFetNO3Qiecsop4vP53A6npGy66lKtPOvAg+SAM85wPu/8wzUS2T0wfN1Y25IFe78DUJpor1GMAutfl7tfHCirl5fP4HyHAkZbDSATLv/jj+MDqPsT5hSvqq/LyLkyPLVaGv/0z3jZCpty+lGHiTmm+KeYpK0GgMJqr5ctWybFqOiS4old+r3e4f0RE7fLxdAATz31lJx33nnxucRVr3aVJA8EAsOuQ22bant1cZFPFxg7ulu0smFVSXlZmWvxAEA+yLe2uhRYYXXOHRgi3VfXEP838E0YK7JyICkebW7h3weAg/YaxcTv8yetM0yTF5ZR8GirAaTDHjSnuDehp7gnEMhI++KZf2jyys1vi2/CNCkVtNUAUBh8RdpWm1JkKisrtXIwGBzWfonbJdaTaS+++KK8//3vjx+3qqrKGS5n7ty5UoyauvSkuNeuci0WAEDpzqFqJbzzZtaMiX/2TZ6qfRdpyd1UKgAA5IrXTE5+R62ELnEAAJScgaS4PyEpbvgzkxgwq+vFU6ZPLxnZ+HZG6gYAACWYFFfJ5cF6enqGtV/idtXV1ZItr7zyipx22mnxIdpVAl4lxN/97ndLsWrpHeh5pwTMwTPoAACQfXZPp4g90Etc8dSPjX/2zZitfRdp0+cfBwCgGHjM5McAMUt/QA8AQCnzJbwrZoxgVM/91l2jJ9gjjeszVjcAACixpPi4ceO08o4dO4a1X+J2Y8cOPCTPpBUrVsipp54q7e3tWkL8Pe95jxSzPcE9WrnCQ1IcAJBbVltz0jqzbuC6wTd7kfZdrNcQq6vvfA0AQLHwGMmPAaI2PcUBAOjnS+gpbmYwKe6t10cnjW7fnrG6AQBAiSXFFy5cqJU3b948rP0St1u0SH8wnqmE+MknnyytrX29pisqKuTBBx+U4447TopdR7hNK1f76lyLBQBQmqzWXftOis8/JOn76LrXsx4XAAC55EkxfLplWa7EAgBA/rCHTIobgbKMHcU3rl4rR5qT71MBAEB2FF1SfMmSJUmJ6OFI3G7x4sVZT4irHuLHH3+8lIKuqN7TrjagXwACAJBtVnuLvsKwxageOB95xk0R058wv9u6lbkKDwCAnDANfSoRJUpSHACAOH/Uzt7w6RMmaOVIC6OTAQCQK14pMtOmTZM5c+bI+vV987E89dRTw9pv8HZz586VqVOnZi0hroZMVz3ESyUhrvTGOkQGdUgYU05SHACQW1abnhQ3vSJGwryqvhqvhHYPDCEb2bwuZ/EBAOBWT/FYCQ6fHoyE5e3d2+XtXY2ypb1ZeqNBCUZC0hMLOj3nvaZXvKbHWXym1/l76/vsk4DHJwGv+umXMp9fyrx9i1pXrj77As7PCl9AKvx+KfP4pdznEzPFfO4AgDzsKZ5wWjTLyjN2FO+06SLyYrwcbQ1mrG4AAFBiSXHl7LPPlh//+MfO5yeffFIaGxtl+nR1wZGa+n5wUlztnykkxPtE7E6tPL5ijGuxAABKk9XRdy7uZ/qTe8r5GioltLsjXo5s3ZKT2AAAyBVvqjnFLb1HXDF6ees6ufW1B2TVntelJbJBLE+bGEZue8jbtkfE9oihfopHDPE6n52fzlvkRt//nN78pvO5b505aP2gsvpp7N1HDQQY/9z3P7Vtv+QBAgZ9N+hz4nd9+yZfMw21r15O/C5x533FMDLd3d3yyz8/klYdAEqbbQTjLVHS8OkZTIr7ZszTypEuS2zLSnphGwAAZF5RJsU/+9nPyvXXXy+xWMx5w/uqq66Sm266acjtr7zyyvgcah6Px9k/E1599VU55ZRT4gnxqqqqkplDPFHM0JPik6rGuhYLAKA0WZ36sHRmIPmhg29cg8g7g5LiTTtzEhsAALniSfHQPRpLePpfJKKxmFz/3D1y97rbJeTZOPCFGi3GhXgMIyailr3Uqwg5ex0hnQMVwjsTZSLtpTfgAYAMGvzOWGJPcaOsImPH8c3Sp+y0Y4bEtm8U79Q5GTsGAABIrShfQVu0aJGcd9558fLvfvc7Z0nlN7/5jdx8883x8vnnny8LFy5Mue2mTZv63sLeu6hth/LGG284CfE9e/bEE+JqDvFSTIj3REIinl5t3eQakuIAgNyKdQ4kuxUzkDx8rG/yJK0cGdRrHACAYqCGAE8Us4tvTvF7Vj4vS299n/xx41V6QhwAgP1I6ilensHh02ctETH0t40i697IWP0AAKDEeoor11xzjTMkev/c4hdccIHcf//9cu6558rkyZNl27Ztcuedd8oDDzygzSV+9dVXZ+T46jgtLQNzl44dO1Z++MMfDnv/CRMmyG233SbFYEvCHK7KjLpxrsQCAChdVpc+aokn4Evaxjd9pog8Hy9HmN8NAFAKPcX3jpxWLL3Dv3j/j+XFtjvF8O7/z2VbXvFYtWJKmXgMn7OofuS2bYstMWexxBLbVp+t+DrbUBmTqNhOz+++HuBGQpIDAFCYAglJcTOQuZ7iRqBMvBWGRLsH1kU3r8lY/QAAoAST4ioJ/fDDD8tpp50mGzf2vRV+3333OUsqs2bNcrZX+2VCT09PUi9ztQzXjBkzpFg0tjUnrZtOUhwAkGNWd5dWNstSJMVnLdDKkW5b7FDQeXABAEAx8KZIiltF0lNcJcQ/8OdLpDHyeIo5tFXuukKm+I+QwyYcJkdOWSIHTpgps+rHi5mheVzD0agzUppagtGI9EbC0hsJOT+D0b4lFI1IMKZ+DpRDsYhErEj83yJmWc5PlZhXvfid/zll9Umc6d9U2VmnUvXO576fdnzbgbjUNvHPg7/Y+23K7RLGTNd3039fhjxWYh2Dy0Psk6q8P+rP1N7WLrV1tfuc/xwAhqOhrEEqI2rE0YH2xCivzOgxfLV+iXaH4+XIls0ZrR8AAJRYUlyZN2+eM4z5t7/9bbn11luloyN5CNTa2lpnqHXVi1sNcY7M29qxS18RK5cKX8CtcAAAJcrq6taT4uXJiW7fvEP0FbYh0Y0rxbfw8GyHBwBATphGiuHTi6CnuEoU9yfEE3mjk+TDc86Xrx5zTlbvRf1er7PUZTh5gn2LRCLOdHVnnHGG+HzJLz0CwEjYliVvf1OfhjPTSXHvmCqR7X1TbirRpu0ZrR8AAJRgUlxRie6f/exn8eHUVW9tNax5Q0ODzJw5U0444QQJBIZ3U6y2T36rOrWR9Aovdk1dAxd5imnz8gEAIPesnl6tbFYkJ8U9U+eK4bHFjg30Coise5OkOACgaHjN5J600SLoKf6Vh3+RlBC3bUMOrf6w/Oas/+bFbADA8ITUfaN+rjTKM/ss0ze+QUQGnpdGdu7OaP0AAKBEk+L9ysrKnKHUkXu7evQLO79R41osAIDSZfXq84OblcnzwhmmKb5qU8JtAy/BRTYxvxsAoHh4UvYUV/NiF677V78sTzT/XoxBo6DbtinnzblcvvGeD7sZGgCgwFjd7UnrzIoMJ8UnTRaRtfFyZI8+1RcAAMiOzEycBexDS2+bVi7zVLsWCwCgdFm9Ia1sVqYeAs83Rk+WM78bAKD45xQf2RzO+aQ92COXP3+pGKae2D9vzndIiAMARszuSU5QGxWZfZbpnTpDK0fa9XtVAACQHSTFkXVtIX349CpvnWuxAABKlxWMaGWzMvWDDd+4eq0c2bEzq3EBAJBLZoqkeCQWlUJ12WO/kZi3WVu3qPws+cZ7PuJaTACAwmX3dCatMzLdU3zWfK0c7bbFjoQzegwAAJCMpDiyrjOiDztU6ycpDgDIPSukP/A3q4dIik+aoJUju/URTwAAKGQ+T6rh0wtzTvHtHXvkmV13aev8selyywe+61pMAIDCZge7k9aZlbUZPYZv9oEJBzUkunFVRo8BAACSkRRH1vXGOrRyfZneAw8AgFyIhfRhVc3q1A82fNNmauXInp6sxgUAQC55U80pbhdmUvzrj/5UxKOfp/976WVSGQi4FhMAoLBZKYZPl0B5Ro/hmTpXDFOfuiRCUhwAgKwjKY6sC1p6Unxs+RjXYgEAlC4rrD/wN2uGSIrPnKeVI52W2AXagw4AgOHNKV5457mt7Xvkjc4HtHUNcoR89MBjXYsJAFD47F49KW54bDFSnDvToerzVhnauuimNRk9BgAASEZSHFkXNfS5eCZWNbgWCwCgdFkJU7R5alOfj3zz9KHs7JghsS08oAAAFO+c4oU4fPq1z/5JDDMSL9u2KVce93VXYwIAFD67Vx+BJMUAKxnhqyvTypGtjdk5EAAAiCMpjqyyLEtsU5+LZ1L1WNfiAQCUJjsSdpLbg5lDJMW9sw4QMRKGslv7RlbjAwAgl2zbKOjh09V95jNN92nrxpmHyXGzlrgWEwCgOFgJc4pnLSneoI9cFmlqys6BAABAHElxZFVTV5sYhj6H6/Taca7FAwAoTVbbrqR1Zl3qpLjh84svYSi7yMa3sxYbAAC5V9hJ8TveeEqiXj158OnFH3ctHgBA8bB7e7Wy6dXPmZninaB3Goru2pOV4wAAgAEkxZFVm1qbk9bNqBvvSiwAgNJltaZKio8b/lB2jZuyEhcAAK5I6Cmuel4XklvfvFMre6Lj5fzDTnYtHgBA8bCDCcOnZykp7ps8RStH9ug91AEAQOaRFEdWbe3YrZVtyysNFVWuxQMAKE0pe4rvY+QS39iEoex27MhKXAAAuCNhRBRLH90rn7X1dktT9BVt3dHjz0w5VzoAACNlB/We4oY3O+cX37TZWjnSEcnKcQAAwADuGpFV2zv1pLhpVfGwAgCQc1Z7i1Y2PLYYAb03+GC+SRO0cqSZoewAAMXELNie4r9f/ogYZkSbH/1rRzN0OgAgM6xQSCubWUqKe2cu0MqxoCFWV3tWjgUAAPqQnURWNXfrSQivUe1aLACA0mW1t2pl07fv7X1Tp2llhrIDABSXwp1T/JFN/9TKlfY8mdMw0bV4AADFxQ4GtbLh9WTlOL65Byati65/IyvHAgAAfUiKI6t29+o968qMGtdiAQCUrlhnm1Y2A/u+BPLNnKuVI+3RrMQFAIAbjMQ5xQskKR6MhGV7eLm2bum441yLBwBQfOxQQlLcl52kuGfcFDF9trYusvHtrBwLAAD0ISmOrGoN6j3zKr36HK0AAOSC3dWhlU3/fpLic5ZoZStiSKx5S1ZiAwAg9wqzp/gdrz8p4unR1n320DNdiwcAUHzshOHTDZ83a8fy1eh1R9aTFAcAIJtIiiOrOsJ6z7wqH0lxAEDuWV2dWtkM7PvBhnfeIUnrImtey3hcAADkRVK8QOYUv2/dY1rZH5smh0+Z41o8AIDiY4UT5hTPYlLcP65KK4c3bczasQAAAElxZFl3tF0r15fVuxYLAKB0WT09I0qKmxXV4q1IGMpuw+qsxAYAgNuPAgpl+PRN3a9q5YPqj3UtFgBAcbJDYa1s+LPYU3zKBK0c3t6UtWMBAACS4siyoKUPV9tQPsa1WAAApcvq6dbKZsC/3318tfo2kUbe2gcAFIvCm1N8dfNWiXl3auveP3+Za/EAAIqTHU5Mivuydiz/jJlaObxTf44KAAAyi6Q4sipi68PVjq8gKQ4AyD2rp1crm2XDSIo31GjlyBbmFAcAFGdSPGrFJN/9ZeWTWtm2AnLG/MNdiwcAUJyscEQrG/793zuOln/+AVo53BYVu0CmNAEAoBCRFEdWxcwurTypeqxrsQAASpcVDGpls7xsv/v4pk3SyqFteu80AAAKlZHwKCBWAD3FX9zxslauMxZImS97iQoAQGmyE5LiZjaT4ov1l7vsqCHRzUzbBQBAtpAUR9Z0hnrFMEPauqk141yLBwBQuqze0IiT4oE587RyuFl/0QsAgIJlJw6fbku+29r7plY+sIFe4gCAzLOj0Zz1FPfOPkgMj34OjqzUXwIDAACZQ1IcWbO5dVfSuul1JMUBALlnBfWkuFFRsd99/IsP08qRTlusHn1aEAAAClNCUjzPh2p9q6lRLK9+f3nG3GNdiwcAULzscEJSPJC9pLjh9Yq/1qOtC69dmbXjAQBQ6kiKI2s2tzdrZds2ZHotw6cDAHLPDiUMgVc+jKT4QUcnVGJI5M3nMx0aAAA5ZyQkxfN9+PS/rtLnE5dYuZw2T395DQCATLAjMa1sBPY/ylg6fOOqtXJ44/qsHg8AgFJGUhxZs72jRSsbVqV4PfrbjwAA5IIV0t/2Nysr97uPZ+xk8VboQ9mF3mIoOwBA8T0KsPI8Kf5K0wqtXOdZIH6v17V4AADFy4rqSXEzEMjq8fyTx2vl8LadWT0eAACljKQ4smZHlz68nceuci0WAEBps0IJDzaqaoa1n39suVYOr307o3EBAJAPPcXzPSm+I7hGKy+qP8i1WAAAJdZTvEy/J8w0/8yZWjnc3J7V4wEAUMpIiiNrdvXs0coBUx8OCACAXLESHmyYlcN7Ucs/JeGt/c1bMhoXAADuKJzh09t6uyVkbtPWHT3lUNfiAQAUNztq5TYpPm+xVo60RsW28ve8DABAISMpjqxpDbZq5XKz1rVYAAClzQrrw6Cb1cM7JwVm6W/th3bo5zYAAApRIfUU/8fa5WIYA/HZtiHvW3CkqzEBAIqXFbVzmxRfdHjC8Q2Jbl6d1WMCAFCqSIoja9pCeuKgykdSHADgDiuil83qumHt519wgFYOt4R5ax8AUHSPAqKWPqJKPnmmUZ9P3BebJOOGOQ0KAAAjZSckxc2yiqwezzvvUDE8+jHDrz6T1WMCAFCqSIoja7oi+hw4tYHhJSAAAMgkOxoVO6b3iDOqhpkUP1DviWZFDIltZl5xAEBhMwz9UYBt6w/j88nbrSu18sSyea7FAgAofnYsoad4eXaT4obXK4EGn7Yu9NarWT0mAACliqQ4sqbX6tDK9YF612IBAJQuu3NP0jqzdnjnJN/8w5Pe2g+98XzGYgMAwB2FM3z67vA6rXzA2ANdiwUAUPzshMFTjEB2h09XAtPGauXgWv3cBwAAMoOkOLImnJAUH1/Z4FosAIDSZXWkSIrXjBn2W/v+eq+2LvSmPowrAACFPqd4LE+T4pv2NIvl3a2tO2GmPvcqAACZlDijiFFemfVjBubM1sqhrbuyfkwAAEoRSXFkTdTo0soTq0iKAwByz2pvSVpn1upv4u9LYKp+/gqteScjcQEAkC9J8XztKf7Q2pe0sm35ZNlseooDALLDjoRFLP0caVZUZf24gSWHaOXwrrAzDRgAAMgskuLIimgsJrbZo62bVDX8BAQAAFnrKW7YYlTWDnv/svlztXJwc3OmQgMAIC8eBeRrUnz5Dn0+8XJ7ulT4Aq7FAwAobnZPZ9K6nPQUP+IErWxFDYmsWZ714wIAUGpIiiMrtra3iGHoD1Zm1I9zLR4AQOmyOtu1sukVMczhXwKVHbJUK4d2RcQOBTMWHwAA7vcUtyUfbexYq5UnlevDywIAkPWkeEV11o/rnbVETL9+Lg4tfybrxwUAoNSQFEdWNLYnz30zo268K7EAAEqb1dmmlU3fyPYPHHWaVrYtQ8KvPZWJ0AAAcIVRID3F90Q2auWFYxa6FgsAoPhZKZLiZg6S4uql7bIJ5dq60KrXs35cAABKDUlxZMWWDj0pblsBqS2rcC0eAEDpsro6tLLpG9nlj3fyLPEmjJgXfJmkOACgcBlGYk/xmOSblp5OiXr0+8p3TT3AtXgAAMXPrZ7iSmCa3pkotGFTTo4LAEApISmOrNjesVsre6wq12IBAJQ2u1t/sGH4R375UzalRisHV76VdlwAALhHT4rH8nD49CfWvyGGMRCXbRty4qyDXI0JAFDc7N5u95Li8+Zp5dDW1pwcFwCAUkJSHFmxq2ePVvYZubmABAAgkdWlJ8XNgHfEdZTNmaGVgxu3ph0XAABuKYTh01/Y9qZW9sbGS30FL1sDALLHDiYkxQ1bDJ8/J8cOHLRUK4f2RMXqJDEOAEAmkRRHVrT06knxMpOkOADAHVaP/mDD9I88KR448BCtHNrRI7aVfwkEAACGw0joKZ6PSfF3Wt/Ryg3+Wa7FAgAoDVZvj1Y2Pbk7dtnR73WS8HG2IcFnH8xdAAAAlACS4siKtlCbVq701rkWCwCgtFk9CQ82ykb+pn/Z0hO0cixkSHTda2nHBgBAPvQUt/Nw+PSm3g1aeVbNXNdiAQCU5vDpxsjfpx41s368BMboBwy+9HTuAgAAoASQFEdWdIT1pHiNn6Q4AMAdVk9v2klx35KjxPTpCYPepx5IOzYAANxgGAk9xSW/eopHYzHpNfSpSg6buMS1eAAApcFO6CluePTzZbaVzZ6glXvfWpXT4wMAUOxIiiMrumPtWrkuUO9aLACA0mb1JibFAyOuwzBNKZ+uz2Pa8/KLaccGAIAb8n349Be3rhHDDGvrTpytT2UCAECmWcGEpLg3t0nx8gMP0Mq9m1tyenwAAIodSXFkRcjq0Mrjyse4FgsAoLRZQf2hullRPqp6ypfM18q97zSmFRcAAG4xDE9eJ8Wfb1ypr4hVysJxU90KBwBQIuxgwgvVuU6KH32yVo50iES3rs1pDAAAFDOS4siKiN2plcdXNbgWCwCgtFnBkFY2KipGVU/FUcdr5WBzRKy23WnFBgCAG/K9p/jK3e9o5UqDhDgAIPdJccOb20fngaWniOHRp+0KPs20XQAAZApJcWSFZXZp5UkkxQEALrFDEa1slo8uKV6+7EMixqAHFLYhvU/dm254AADkXL4nxRs7N2jlieWzXIsFAFDCSXGfPrJKthmBMimbVKat633lhZzGAABAMSMpjoxr7ekSw9QTENNqx7kWDwCgtFmhqFY2KytHVY9ZP17Kxvm0db3PP5FWbAAAuMFIeBRg2XqvNLe1RvQpSubXz3MtFgBA6bDDYVd7iivl86Zr5d7V63IeAwAAxYqkODJuQ+vOpHUz68e7EgsAAFYoppXNyupR11U+f5pW7nlrzajrAgDALYah9xS386ineGeoVyKeZm3d4ZMWuRYPAKB0WKGgVjZ93pzHUH74Uq3c09gtdm93zuMAAKAYkRRHxm1p36WVbdsjE6vqXIsHAFDarEhCUrxq9EnxiqVHauXexk6xI3pvAgAACq6nuORPUvy5zavFMPR4jp91gGvxAABKhx0KaWXDhaR4xemf0Mp2zJDef/0l53EAAFCMSIoj47Z37tbKhlUppsmvGgDAHVZYHxLWrK4ddV3lyz6o1x0xJPj4PaOuDwAAN+RzT/GXtq3Syka0XiZW17sWDwCgdNihsOtJce/UORIYq89l3v3EwzmPAwCAYkSmEhm3s6tFK3vtKtdiAQDAiuhls3L0SXHf/EPEP0a/fOp67O+jrg8AADeYeTyn+Ord+tQkNZ6prsUCACgtVuKc4n6fK3FUHjBLK3e//o4rcQAAUGxIiiPjdvXs0coBs8a1WAAApc2ORp3h5gYzatLrbVZ1YMIDihWr06oPAIBcM4z8HT59a89GrTylcrZrsQAASoudkBQ33UqKH3eiVu7dFhSrTR+ZEwAAjBxJcWRca7BVK1d4Rt8jDwCAdNid+otaillTl1adlSedrpV7d4QktnNzWnUCAJBLhugvjFl5NHx6e7RRKy9qmO9aLACA0mKH9WHGDL/flTgq3vtJEWPQKC62IT3/uMOVWAAAKCYkxZFx7WE9KV7tIykOAHBHqrfpzdqGtB9QGB79AUX3fbelVScAALlkJvQUt/Nk+PSdXe1ie/UX2g6ftMi1eAAApcUOR7WyEXAnKW7Wj5fyyWXauu4nHnUlFgAAiglJcWRcZ7RNK9cG0humFgCA0bI69Re1FLN2bFp1mtX1UjGzSlvX9dSTadUJAEAu5WtP8ac2vqmVbduU42YtcS0eAEBpsaJ6Utz0B1yLpfKQBVq5c8V6sa38OF8DAFCoSIoj43pjegJiQuU412IBAJQ2qyNx+HRbjMr0RzCpOvIwrdz11naxQ8G06wUAwI05xW3Jj57ir2xfpZW9sXFSW1bhWjwAgNJiR/Kjp7hS/cFPauVIp0jouQdciwcAgGJAUhwZF7bbtfKU6gmuxQIAKG1Wd6dWNr0ihpn+5U/Vh87TyrGgIT0P3pp2vQAAuNFT3M6TnuJrWtdq5XrfdNdiAQCUHjsS08pGQB/CPJcCx5wpvmp9Xeff/uRWOAAAFAWS4sgoy7LE8ugJiOm1JMUBAO6wO/UXtQxvZur1H3SMlI3XK+u496+ZqRwAgBzPKW5JfiTFm3o3auUZ1XNciwUAUHqSk+LuDZ+uXuauPnyutq7zxbdciwcAgGJAUhwZtaltlxiGfgE5t2GSa/EAAEqb1d2hlU2f3jMuHTXvOVwrd76+VexgT8bqBwAgdz3F7bx4wbrb3qqtWzJWn08VAIBssqL6S2JmWbm4qfr9H9bKoRZLwq897Vo8AAAUOpLiyKi1LduT1pEUBwC4xeru0sqmP3OXPtWf+A+tHAsZ0n3f7zNWPwAA2WIaHq1s50FP8TXqXtLTra07ZvoBrsUDACg9dkJS3HA5KV5+6sfFU66/uNZ++y9diwcAgEJHUhwZtbmtSV8Rq5QKn3tDDQEASlvinOKGX08CpMO/5Cgpm+DT1rX/318yVj8AADkbPj0P5hR/etObWtm2fHLk1HmuxQMAKD12VE9AG2UVrsXiHN/nl5pDZ2jr2p98Xexo1LWYAAAoZCTFkVFb2ndqZa9d61osAADYvb1a2fRnaFLxvWpOOFIrd76xU6Lb9flQAQDIN4Zh5F1S/LWmt7VymT1ZvJ7MvcwGAMD+WIlJ8XJ3e4ortZ/5f1o50iXSc+9NrsUDAEAhIymOjGrq3qWVy81612IBAMDq0YdhNQN6z+501V7w32KYAw9ObMuQ9t/8MKPHAAAg08yERwG2uD+n+Ib2dVp5rF/vGQcAQLbZMb1sllWK28qO+5AExuovibXdfYdr8QAAUMhIiiOjdvfqSfFq3xjXYgEAwErqKZ7ZpLh36jypPmCctq7tkefFttzvcQcAwHB7itu2+0nxXeHNWnl27VzXYgEAlKbEpLjbw6c7MZim1J16jLau861djFAGAMAokBRHRrWH92jlugBJcQCAe6zeoFY2yvwZP0b9pz+nlcNttvT8328yfhwAALLXU9zdl7nC0aiEjO3aukMnLnYtHgBA6VEvNiclxcvd7ymu1Hzh0qQRylp/+m1XYwIAoBCRFEdGdcf0pPi4cr33HAAAuWQH9aS4WVaW8WOUv+888dfrl1S7f8ccbwCA/GUY+TV8+svb1ophRrR1x8080LV4AAAlKKRGGdNHUjHKqyQfeCfPkuoDx2vrWh9dIVZnq2sxAQBQiEiKI6OCVptWnlytX7ABAJBLViislc3ysqwMZzfmrGXaup5NvdL72F0ZPxYAAJlgJg2f7m5P8ecb39JXxKpkwbjJboUDAChBVnd70jqzIj+S4krDf35DK8eChrT/73ddiwcAgEJEUhwZFTM7tPL02omuxQIAgBXUe52Z5eVZOU7tf/5QPOV6L7vdN96QlWMBAJAu0/Dk1fDpb+1ao5UrjSmuxQIAKE12T1fSOqOiWvJF2bHvl8rZ+nDuLf/3T7GDPa7FBABAoSEpjozZ1dUhhhnS1s2qJykOAHCPHdKT4kaWkuJmVa00nHm0tq5rTYf0/vPurBwPAIDM9hR3d/j0xq51Wnli+SzXYgEAlCa7pzNpnZFHPcWVhi98QStHOkRar9d7kAMAgKGRFEfGrG3ZnrRufgNv+AMA3GOFo1rZrKjI2rHqL7laPAE9qbDzR/8jtuVu7zsAABKZCXOKWy73FG+NNGrlBfXzXYsFAFCarBQ9xc3KWsknFR/8f1I2yaet2333vyS2p8m1mAAAKCQkxZExG1p3aGXbCsi4qhrX4gEAwArHtLJZmb03/c368dJw1ru1db3bQtL5u6uydkwAAEbDSHgUYIt7PcXbersl6tmlrVs6ebFr8QAASpPdm5gUt0UC2RlpbLQM05QJl3w1aW7xliu/5FpMAAAUEpLiyJjGdv2tRI9FQhwA4C4rovd8MyqzOydc/WU/F1/C6W/nb+/izX0AQF73FLdt93qKP7NppRiGnpR/z6wDXIsHAFCa7GC3VjY8fUnofFPx/s9K1Xz9prPl0bck9NJjrsUEAEChyL8zOwrW9s5mrRww6lyLBQAAxU5IiptZToqbFdUy/ouf1NZFu0Sav3ZeVo8LAEBac4q72FP8pW2rtLIZbZAJVfk1XC0AoPjZvT1JSfF8Nf47PxAZ/EKZZciOS78udlSfPgwAAOhIiiNjdnTrw6dXece4FgsAAIoVkZwmxZXqz35LKqaXaeva/t0o3ffelPVjAwAwqp7iLibF396zRivXeKe5FgsAoHRZKXqK56vAkadIw0kLtXW928PS+sOLXIsJAIBCQFIcGbM7qCfFx5dPci0WAABsyxIr4UV5szr7Pc/UEHuTrvuFGB49wbD9yp9ItFF/8A8AgBsM0XuKi4vDp2/pXquVp1XOcS0WAEDpsnt7tbLpTThX5pmxP7w5eequPz8tvY//1a2QAADIeyTFkTGdUX349Ok1vOEPAHB5Tjhbf5BhVNXn5Nj+g46R8R9fpq2L9hiy7f+dK3YknJMYAAAYisf05EVPccuypNvepK07fOKBrsQCAChtdjBh+PQ8T4qbtQ0y6dL/0ldahmy79HKJ7dzsVlgAAOQ1kuLImIixWyvPGzPDtVgAALA79yStM2vqcnb8+ktvlIqZ5dq6nk290vyls3MWAwAAwxs+3Z2e4i9sWSPiCWrrTpl7uCuxAABKmx3Ue4ob3vx/bF559hel4WR9GPVIh8jWT39Q7F59OHgAAEBSHBmypa0l6WHGgRNmuhYPAABWe2vSOrNmTM6Ob3i9MuW3d4m3Ul+/56n10nLFF3IWBwAAiTyGkRdJ8cc3rtBXxKrkgPHTXYkFAFDarISkuFkASXFl3HV/kvLJfm1dT2NQdnz+fWJHE+YTAwCgxBXG2R15b8X2dVrZtg05aBJJcQCAe6yuFEnx6twlxRXv9Pky5QffFjH1YWmb//yctP44Yag7AAByxEjsKW67M3z6a81vauVac5aYJo8pAAC5Z4dCWtnw6VON5CujrEKm/Po28Vbo5/L2FTul6YL3iW258+IbAAD5iLtNZMTq3fo8cGasTip8AdfiAQDA6mjTVxi2GOUJ3bZzoOK9n5LJFyUPmd508yPS+qMv5TweAACSh093Jym+pXuNVp5VrQ8BCwBArtghfQRMw1sYSXHFN/8QmXr9D8Xw6Ofztn83StPnThM7EnYtNgAA8glJcWTExrYtWrncHOdaLAAAKHZXu1Y2va6FIrVf+h8Zd/bSpPVNt/1Lmr/6Ed7eBwDklJnwKMCN4dOjsZh0y2Zt3eGTDsp5HAAAKHZITxwbPhdvIEeh/IRzZMplFzgvgw/W9sJW2fqR48Rqb3EtNgAA8gVJcWTE9u5tWrneN9G1WAAAUKzuTq1s+sRVDT+4VRree0DS+paH35JtH32PxPY0uRIXAKD0eBJ6iqu0eK493/i2GKY+VO0pcw7PeRwAAChWWD8nmQWWFFeqP/U1mfL1TyUlxrvebpdNZx4v4ZUvuBYbAAD5gKQ4MmJPSH+QP7FysmuxAACQOinu7mWPYZoy7id/lrFnHpz0Xedbe2TTGSdK75N/cyU2AEBpMQzD9eHTn9i4Ql8Rq5ElE6blPA4AAFL2FPcXXlJcqfn8d5zEuGHq5/bQrphs/Pj50vG7q1yLDQAAt5EUR0Z0x5q18sxaHmYAANxlJyTFDb/7lz1OYvy6u2Ti+Scnvb0fbrNl04WXSfOXPsjQdgCArPKaifOk5n749JeblmvlOs/snMcAAEA/O5yYFHd5qLE0E+PTrv6GmH79ntMKG7Ltujtk28feI9GNq1yLDwAAt7j/dBgFT80FFzX1h/cLGma4Fg8AAIrV3aWVTV9iAsA99ZfeKNOuuCjpIYXYhrT88x3ZcMqx0v3XX7kVHgCgyJkJw6fbdu57im/rXamVl4w5JOcxAADQzwpHtLLh90shqzzr8zLz978Uf70+OozS8fpuWX/W2dJy+efE7u12JT4AANxAUhxpe3vXNjHMmLbuoIm85Q8AcJfV06OVzUB+DX9Xde6XZdadt0rZ+OS4Ih0ijd/5uTSefrj0PnKnK/EBAEpp+PTc9hTfsGenRL36FFwnzzwqpzEAADCYnZAUNws8Ka4EjjhRZj74uNQc0JD0nRUxpPkv/5YNy5ZKx01Xih3Re8oDAFCMSIojba/v3KCVbcsrC8YypzgAwF1WsFcrG4H8G/7Ov+QomfnICzL2rEOT5nxTujf1yKavXClb3n+k9Dz8R7Gt3A9vCwAoPp6EnuIqLZ5Lf1v1rH50yydnLDwipzEAADCYHYlqZSNQ+ElxxTNmoky++2mZ/J/niOlLPt+raby2/eRO2fCeQ6TtJ18Tq0efhgwAgGJCUhxpW759tVb2WWPF68mfIWoBAKXJ7tGT4maePtQwyitl3LV3yKxbb5Tyyalj7FrbKZu/+j+y8fiDpPW6S5hzHACQFtPQ79fsHCfFn9/2slaulrlS4QvkNAYAAPadFC+e85JhmlJ78Q9kzn3/J7WHT0y5jUqO77jpIVl3zFJp/s+zJfza0zmPEwCAbCMpjrS906onxcf4Z7oWCwAA/axgUCubZfn9UCNw5Cky49HlMvGzp4i3InVyIrQrJk2/e1jWHnuMbP/ECdJ118/FDurDxAMAsD+ehOHTJcfDp2/qflMrL6g7OKfHBwAgkR3Rp4Y0/Pl9/zga3lmLZfKfnpCZN14hZRNSj6QW6zWk5bHVsv7cL8rmUw6V1mu/ItHNb+c8VgAAsoGkONK2M6gPnz63doFrsQAA0M/qDRVUUlwxvF6p/++fy5yn/i3jP3aMeMpSJ8fV/G/tK3bKlu/9Sta+6zDZ/skTnXngYs1bch4zAKDwGAnDp+eyp3hTZ6uETP18tWwG84kDANxlRfWkuFlEPcUTlZ9yrsz81wqZcsm5Ehg39GifPVuC0vT7R2XtaR+UTScfIi1XfEFCLz7CtF4AgILldTsAFLZgJCxBY5sM7mdwxKQDXYwIAIA+VkhPihvlZVIozOp6afj+76T+v5ql9YbLpPUfz0ukI/W2sZAh7ct3SPvyO0Wuv0PKp5RJxeLZUv6uY6Xi5HPEM2FGrsMHAOQ5N+cUv+P1J8QwBo5nWx75wCKS4gCAPOspXlYuxUy9kF3z/66Q6i9cLt13/0Jabr7FSYIPsbX0bg1J75+fk+Y/PyeeclsqF0yQiiMOl/JjTpXAESeK4cvP6coAABiMpDjS8symVWKY+pw7p8w51LV4AADoZ4ciWtksL7yHGmb9eGm48mYZ892odN99o+z50x3Svb7TeSiRkr33YcXW1SKPrha56ibxjzGlYu4kKTtgiZQd+m4JLD1ZzLqxuf6jAADyiNf0uJYUf2zz41q5UuZIXXllzo4PAEAqdtQqqaT44PnGq879srMEn39I2m/7pbS/sM55+Xooaoj1jteapeO1h0V+97CYXlvKJpdL+YJZUnbYUik77FjxLX4XiXIAQN4hKY60PNv4ulY2onUyc8x41+IBAKCflZgUr6gs6Lf4qz7xVWeJrHtdOv/0v9L+xAsSbNL/jKmE91gSfmmbiFrkURH5nvhqTSmbXCuBWdPFP3uO+BccJL4l7xLv5Fk5+fMAANxlmu4Mnx6ORmVraLnIoJz8EePek5Njo/ip4XztaFR9EImpn7bY6rM1UFbf2YM+SywmtvrsDAXcV1afnf8m1HaW+m+jf7shhgtW+yeyRrBtX/DD3t52YkpliDqsERxzhPENHQtQeGKh0kyKD1Z29BnOMq6nU7ru+Kl0/OMf0vV2i9jRoRPkihU1pKcxKD2Nq0UeWy0ifxDDY0ugwSeBKWPEP3umeGfNlzGdIYntPkR8kxjNDADgDpLiSMtbu9WFzoBa70zXYgEAYDArHCuapPhgvrkHy5grfitjrhAJv/m8dP3tNul6Ybn0bOoS29r3w4o+hkTabYm0t0nn6jYReUNE/uZ8Y/pt8df7xTe2Snxjx4h34kTxTZkm3mlzxDdnsXhnLBYjUDjD0AMAUjONhPOFkZu5Qf+26t8ini5t3acPem9Ojo1RJpk79oillvbdYnW09i2dbWJ1dYjd3SlWsFfsUFjscGjvEhE7HBYrHBY7oj5HxY5GnGGJ7UhU7JhKSqtEtd2XUFXHiH/em2RVZZXQVr+WTu5673d789NOPlt97s/H2iLzxZD1/32py39jAIqJWVYhpcqsqJaaL1zuLFZXu3Tf+zvpfOQh6V69XaL6aXxIdsyQYHNUgs3NIq82i8hLosYr2/ibv4gnYIuv1ie+cdXinzRevBMni3fSZPFOne3ce3qnLxSzqjbbf0wAQAkiKY60bO1ZKzKok8HMqnluhgMAQJydkBQ3iiQpPpj/wKNljFrUM+L2Ful58A/S8+9npGf1BgnuCDoPIkbCChsS3BmR4M5WEVHLen0DwxZPQMRb4RFPlV881RXira0ST32deBrGirdhnHjGTXIWNUS72TBJPGMmiFHCD5QAIB+5Naf4395RI5YM8EYnyVHTF+Tk2KVMXSNEN78t0W0bJdayU2J7donVukdibW0S6+yUWGeXxLp6JdYdllhvVKywJXZE9fxTe4/sWiI78iEGAKWGl4H7qOR09ae+5izqZanIG89K98N3S/fyVyW4aY9EhpkkH0wNzR5zEuatIivVfec7ycf12+Kt8ojXue8sF091pXhqqsVTVy+eMQ3iGTO2795z7CTxjJ8qZt04MarrneHgAQAYCklxjJplWdJjb9HWHTJhiWvxAIDb1A3isLdzesX0LcgOK6L/3ZqVVVLMzNqG+BDrit3bLcGn75Oe5/4pvaveltC2Vgm3qe5VaTxYtg2JBUViQUtkT1BE1LJHRBr3uZsaOs/0i3gCppgBr5jlXvGUB8SsKBOzvEyMgF/MsjIxy8rFKK9w5n9XLzGo3v1GZZWYlTVilFc5D2TMymoxyiqdRHvfUikSKOfhBwCMgGnoc4rbQw27nGFvd7ygPYVYWHNUTo5bzGLNWyTyzqsSWb9KwpvWS7SpSaJ72iTa1iXRjpBEuy2xIiM995OEBoDAoUzvkUjdc/kPOc5Z6veui25cJb3PPCC9r7wgve9sktDOHokF0z+PqBe2nanA4vedrcMI0BbTqxLqhpjOvadHPAGfmGU+576z7/6zXIyygBiBQN/956D7UHV/6dyDlleJUV7Zd+9ZWS1mebUYVbVi+NW9a7mI+sn9JwAUJJLiGLUnN74l4unV1p0w6xDX4gEUJ8EY6hWrSw3p1yZ2b5fYwaBIJCh2SA3tF+wb1q//Z0QN9ac+h/d+7hv6T6LRgbL6rOamU/PIOfPR7U1k9ic0B5WdeeucdXvnr1PfOXPU7V3vDP83eLjAvqEB+55Dqi/3/jn653IbNCSgU3d83cC28Y+D94mvG/yXY+vTxCX8TFo/rL/wfRZHXt8o983oce3cPBicr/rgXvatjNeLoZlVNVJK1E18+Wkfd5Z+VmerhJY/LqFXX5DQ26sltHWnhHd3S6QjzWT5fqge67FekViv+g8ssnfRryHSZtqiOj46i0f9NPp+ek0xPOqzufdz3yKG4WwjqqweaDjb93/eu425t+zx9P30qp+evn08nvh6w+N1PjvbqyGJ1bbO0MTqs6GvMz0J5f59VLD92/cd2ykn7WMm76PiUUP3qD9Pf3s1+CFN/zDJg4ZLdo6RsC7+WXvAo68zBreHzvGcp2NJm2vr+rcbNLyQkXLf4a3b574ZFo3FpG7LmxJeXiaW+jfOhaQevAUu/u9fLIrj36du0yqpCNrSU2bkrKf4vatekJi3SVv3oYWnZv24xSC2Y5OEXntGQitflfD6dRLe0SyRli6JtKte3fv7b6zY/hsEgOzylNsy7qMni3fWYrdDKQjq76laLZ8ZWBdtXCOhV56Q0KrXJLR2nYS27ZJIq3pRy87ueck2xFIjnajbzW71oE0tqqC0Z/7+0xh8/9l33evceyb87Lt/HHQv2n/f2X9P6tx3Dtwj9t2DDtzz9d+/9u2n7juN+M/4vapzX6rWDZQH35MqTn3xe8y+e0tt3aBlYP2g+1Lnx+Cyvo1e18B2fesT9lN/hlTX1SnvHVKsS/VSQqp7whTr4vfCo9g35XGHGZ+RcrtUx00VX6rDDvfvDxiZaFSNvBmQYkVSHKN251uPaGUjViuHTprlWjwoLCrJbHXsFmvPTrHaWsRq2+38jDnz1KmEdqdYnR1idXdLrFsls0NiqfnpQhGx1Fx04ZhYzrx0llhRW+yo7QzvZ6s2O4uJncLG3wtKm1nJnGRmdb2Un3COswxmB3sk8s5yCa9aIeGNayS6Y4dEdrVItKVDIu1BiXapF3nyvA2xDGfu0eS0Tn/vR304fWC4xjtjIfzJ7TCAjDpARH4vIve/y5A/nejJSVL8t6/q/x0Z0To5e/G7s37cQmK1Nkvw+Ycl+OqLElq/XsLbmiXU3LuPHne5PjfbYvpk72KK4TfFVC+cqZfGfJ69P73xxXR++sTw+51RYZyfPr8Y3r6XydTDe8Pr6/us1nm9fS+aeX3Oeufh/t7PzndqX9UFUNWptndeDjMlalmyYsWrcvjSpeL1+fa+QLY3YdD/IF7V5TyY73/BbG8yof9ls711OetM795ERP/3/S++7d036Z9hiH+HlA/szeE9OB/qBamUD+2HV+eIjk8PSABp8E6f7yyVZw+si0Qi8o9775ETZ44Te8MqiWxcI+EtjRJp2iXR1m6JdoYl2mOPeAowV+8/nRfAB+XdB/dUAYBCds3VUqxIimPUXm95TmRQh5npZUeIyY1TyVG9nq3d2yS2faPEdm6T6O4dEmtpltieFom1tUqsvUNiHd2D5qiLiRW2+97azOhDnAK5aAbgGt/8g90OIW+pYeL8B7/HWYZq62Pb10t0w0qJNm2V2K4mie1ulqhq61vbJdrRJbHO4N523pJYmBeUACDfqTu3D7xoy6OH2bKrOrsPb5s6W6Ux/LyWjztszOnizdUIDHkotmtbXwJ8+b8l+M4aCTa2SLhVvciVeP7M3PlUTWfiKTPEU+4RT4VPPFXl4qmq6JujtbZWzPox4qkfI2ZtvfMinVldJ2btGDGr1bqGvJ2rVSVaWvdY4l96svhUUhwAkJcsf7kz9Lpv6Un7fMYY3fS2RLeul+j2Ronu3iWx9jaJtXVIrLNLrK6gRLtDfc8Xg6qDDPedAIDhIymOUdmwZ6f0mBu02/PTZ6e+oEFhUr0G1XBH0a3rJLptk5MEiTbvlOjuPRJtbZdYe49EOyN9b3GOuPcgF6wAcsmWhlMWM/xdGtQDcO/Uec4yHOphht2xR2ItO8Rq3eX0fIu1q1FB9ojV0S6xjva9I4J0iRVSU1qEndFArFBE7LAaClaNBKJGBLH6RgJRQ+CpN/Dzvbc6ABSg2U3ZT4pf++xdYpjqjak+tm3KN44eNM5qCYisf1N6Hv2L9Lz0gvS8vW1vAjzRaM9ztngrDfHVB8TXUCPesfXiHTdevBMniXfSdPFOmyPe6QvEHDMxL5PaAAAo6hzlGT/NWQJHnjKsfayeTrF27xCrbZdY6p6zfY/E2veI1dnWNwJlV5dY3Wrp6VtCaqrE/lEo1dSJMbGiljMSZd8olKq3OvefAFCsSiIpvm3bNrn99tvlvvvuk02bNsnu3btl7NixMnPmTDnrrLPkU5/6lEyZMiWrMaj5gR9++GG588475eWXX5bt27c76ydPnixLly6Vj3/84/Le9763b4iuAnDLiofFMAYenNiWTz59CEnxQhLbvV0ia9+QyIZVEm3cIJFt2yTSvFsiLZ0SaQs5ye7h9fIrjN/ZpPlmnTl/jIGf/fPN9s/10z/vrBoC0JlDdtDcss48Pf1z/vQNp9c/92x8mL7+eX765wnaOzygM1/P3nlo4/P9eLzxuYRSzt8TH/Yvxdw9e78bct6e+L4Jc/b0zzurDSM4aF4hZ86hDM9/01dBGnPgpBrWb4jfv2HPD5TGdkMdfxhzwUajUVn+ynI5/IjDxauGfsxXBXJO2h/v1LnimTTT7TBKijPPdt1YMevGZrReOxQUq7tN7KCaVqNHJNgjdqh30BLsW8LqZ6jvZ1j9DO/9ufdzNOo85bBjMTVptNjqiUfMEjum1quHIWqd1fc51v/d3rJar8p2/zr1cljfT7Ft55qvb8S8vp9O2Qm+f53aZlB573XiwCh7yd8n1zPw1UAd+xmlb4jv4vUMY9vE77TNRnjsYe+rfV8cbRKQbya3iMj87CXFeyIheXz7X7WnD2OMQ2TJhGlSzMKrXpSef/5Nel56SXrW7JBIR5oVmrb46zwSmFgr/qkTxTd9pvhnzhPfvAPEN/8wMcorMxQ5AACFw6yoFnN6tcj0+Rmv246Exe5WifV2sXva++41I+ql7pBIpP/eM9S33d6fEv8c6VvvLBH1ICj+uf9e07mvVPeizj1l332nc7/o3GfG+u4BnXtPu297537UHviptlXrne/7P8vA/anzh9j7I9V9aby890OqbeM/997fJpT7v+9blbzfwPYp7l+H9Y8w0n+1ke/rViy2S39eoNTl8ZP4zPj1r38tX//616W7u1tbr5LSann++eflBz/4gVx33XXyxS9+MSsxNDY2yvnnny9PPPFE0nfvvPOOs6ik/Yknnii33HKLTJ8+XfLds9uf0spjzAOktqzCtXiQTF2YRda9JuHVyyWydrWEGxslvL1ZIi1dEulQvfD292A5tw+e1VB+ztx0AVM8AY+YZV4xy/xilgfErCgTs6xMjLKAmIGAmOUVYqhyeYWYFZXOAyBTLZU1YlRWiVFRI2ZVrRhVtc56o7xKxF9GrwjkDVMN8bgrzBCPwAgZgTLxBCa6HQbyjPPQJ4tD8j780EPy3jPOSK+9zmKMGWMXQIz8PWZE04UflLZ/b4mXp7Soh6LZi/s7//ydxLzN2rpzF35Eiq0dirzxrHT/817peWW59KxrlmjXKCszbAk0eCUwtUECM6dJYOEB4j/oXeJf/C5nuhMAAJAbhs+flZe9ASCfRSIReeehh6RYFXVS/Morr5QrrrhCWzdv3jynd/bWrVtl/fr1zrquri75j//4D9m1a5d85zvfyWgMKvF+7LHHypYtAw8dqqurZfHixc5bV6tXr5bOzk5n/eOPPy7vec975IUXXpBJkyZJvnqjaZPsir2qdYY8elLqOUiRXXZvt4RXvyKRt1dIeP07Et7cKOGmXRLZ3S3hDvVWoJHThHfSHHXV5eKprhRPbY146urFM6ZBPA3jxVPfd0Fp1o8VT/0EMevH84AHAACMSjZfehs8Ukxax+HFPOSRwOzZIoOS4pP37G+oidHb3rFHHtvxR5FBU4f7YzPk/x3xXin0JHj45cek5/H7pWf5a9KzYbdEe0Zxj2XaUjbWJ2UzJ0jZ4kVStvQ9EnjXac4LvgAAAACAzCrapPjf//53LSGuktB//OMf5bDDDouve+WVV+Qzn/mMk5hWLr/8cjnooIOcIdUzwbIs+cAHPhBPiKvhjb/3ve/J1772NamsrIwn5FUvdZXAV0ly1atcHf/FF18UM08fnl3+xI1imGpilT625ZHPH3amqzEV+9zekbdfkdDKVySy7m0Jb94s4R27Jby7RyJd+xriPEOJb8MWb4Uh3iqveGvKxVtfI96GMeIdP148E6eIb/JM8UybLd4pc8WsbcjMMQEAAABkhX/BASLylD58epZ6uF/wwPdEPPqobf916CXiVVP4FBA13GnohX9IzxMPSs+K16VnU5vEgsaI771Mny0VM2uk4pAlUnHcaVJ27PsZ9hwAAAAAcsRbrN371ZDp/aZOnSrPPvus1NfXa9sdccQRznqVCFfzjitqvzPOOCMjc7zedtttTuK93zXXXCPf+MY3tG2qqqqcRHl5eblceumlzjq1j0rgn3feeZJv3mpqlPXBf2m9xOeUnSTzxuZvz/ZCoOY2ja59VUIrX5LwmlUS2bxZQtt2SmTX3h7fWUx8e8pt8dX6xddQJb7xY8U3ebL4ps0U78wF4pt7gHgmzhIjn+c8BgAAADBsgQPfJSL/Gy9XhEXqezKfFP/WozdLY0SfQqxeDpNPH3qi5Durs1WCT/1dev79hPS+sVp6NnWIFRl5EtwTsKViVp1UHHawVCx7nwSOOt0ZihUAAAAAkHtFmem66667ZN26dfHy9ddfn5QQ7zdmzBjn+4997GNOee3atc7+n/rUp9KOQ81V3u+AAw5weogPRSXL1bzib731llO+6qqr8i4pHo3F5KJHLk/qJX7Vsv90Na6CmuN7/esSWfOGhNetlsimDRLa2iSR5k4Jt8XEztJQ56o3gr/eJ/7xteKbOlH802eKb+Y88c1ZLN45BzE0HwAAAFBCvPMOFfHYIrGB+4wpLZlNiv/k2Xvkvm2/0F6mti2f/OjEvhfB801k40rpffxv0vvSC9KzplGCO8MJU1ENMwlebkvlnLFSftjBUnnSWeI//CReMAYAAACAPFGUd2d33313/LOaP/xDH/rQPrc/++yznTm8d+zY4ZT/8pe/pJ0UX758uWzYsCFevuiii/Y5HLr67sILL5SLL77YKav5zl999VU59NBDJR+ooeA/8X/flVZZoa2fVbZMDpo407W48m2Y82jj2xJZv1IiG96RyOaNEt6+QyLNrRJpDWZ1qHNPmUp8+8U3oV78UyeJf9Zc8c9dIr4lR4pn8qyszrUJAAAAoHCoJK1d7xFjt5Uwr3j6ukMh+fLDN8iLbXeIYep1fnTmJXLMjEXi9ghdkVUvSvDlJyT41usSWrdZgts7Jdo9uvszb6VIxbzxUnHE4VJ58gfFd9Cx3HsBAAAAQJ4quqR4b2+vPPbYY/Hy6aefvt+h0NX3artbbrnFKT/66KMSDAalrKxs1HHcd999WvnMM/c/57bapj8p3j8vej4kxd9o2iT/+cgVsichIS6xcvnRsq9KMVNzx8V2bROreYvEdm2XaNNWiW5vlOjOnRLdvVsiezok2t4r0c6oxIL7e3iSZuI7YIt/jF/8E8eIf9oU8c+ZJ74FB4p/8ZHiGT8trboBAAAAlA57XJUYuzvi5ckttvMi9L5e5N6Xps5WueH5v8ojW++SmLdZjIRbn7mB98p3l6U/GttwWF3tzsvK0Q2rJbx2pYQ3rpfwtiYJ7+yQcFtU7Ojo78t8NSIV8ydJxZFHSsXJHxL/YjUUPQAAAACgEBRdUnz16tUSCoXi5WOOOWZY+6nt+pPiKiGu6kknIa16efebNm2as+zP9OnTnfnPt27dmlTHSK1Zs0bq6uqGvX1PJCwdvT1i1JRLjxmTdS1b5fXm1bK69TXpMFaJYdhO4lf2vuxv26Z8euoXxdPaLatbV++3/oaGBhk/fnzK71Sv+HA4POxYbcsSiYZFQkGpqQjIxDH1IqFesSNBJ4ltq3//SFC2bG6U3p4eZ1tb/Qz2iN3bLXZvj9jBXrGDwb5FbR8Oi90bEqsnKBKMiDdkSVXMFMsJS39o0hmLScQeXU8Kr2FIjceT8rtuKyZh0xKzxiNmQ7V4JowTz9Tp4pm1QMy5B4mMnyK96sWPxB1busTfuV7mzJmTst7m5mZpaWkZVbzqodiCBQtSfrdnzx7ZuXOnjNaiRal7iXR0dMi2bdtGXe+8efNSvgjT1dUlW7ZsGXW9s2bNSvmijGovNm7cOOp6VdtQVVWVtD4ajTrTOYzWlClTpKamJuV3qn0brQkTJjjTTqTyzjvvOA9TRyOTbcRgtbW1zoghqWzevFl6VNswCpWVlU6bnUpjY6N0dyd1N0r691W/j2+//bb2+1pRUSEzZsxIuc/27dulvb19VPH6/X7aiL1oI/rQRuR3GzEU2gh324impqZR10sb0Yc2In/aiK2VlVIV3RMvj98k8vqN3xdvVa0YFar7c5WI6RXbMMTw+2X8tKnSGg5KWzgku8M9sr5zl2zs2iEbd2+WrR0bJOTdIWL0TbOl7p6MvbdL6ueE2EFyxVGny8rnn1RDkMXvKU2xZd6c2c46dS+n7u/sUI/Y4ZC07mqWXc27RCLq3i7cd5/X1SF2d1ff0tMjdne3cz9n9YbE7g6K3R0Wq0fVZciYIV6MD1u2dFkD04Htmy2eeo94Z4wX35IDxXP0aTLvpPdxHZGHbcRQ19b9aCP6cB0xgHuN0moj9oc2og9txADaiD60EX1oI/rQRpRGG7FoiD9bUbGLzB/+8Ad1ix1fnn766WHt99RTT2n73X777WnFMXv27HhdJ5544rD3W7ZsWXy/uXPnjvi47e3t2p9jpMs3J0+wXzlwobMsH7SsOGChXefxjLreCxsa7NWLFjjLqsHLwgX2HL9/1PV+vK7OXrVgYcplaXn5qOs9tap6yHrVd6OtV8W0eskCe/3Ri+0tH3iXvfPiD9qt111id99/i33IgnmjrjcQCAz5O3H88cePul7DMIas9yMf+Uhav2tD+epXv5pWvatWrUpZ7w033JBWvffee2/KetX6dOpVcaWi/hzp1Kv+HoeSTr3q330o6vdltPWq39OhqN/v0dZ7wAEHDFlvbW3tqOudMmXKkPWq70Zbr4ppKOrPMtp6aSMGFtqIvoU2om+hjehbaCOGbiPC4bDz3/F1112XVr20EX0LbURhthHqfm2oe6OLGhpGXW+9xzNkvd8ZPyGt37Wh6r1+8uT06uU6wlloI/oW2oi+heuIgYU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      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_system.nsteps = nsteps\n",
    "# perform closed-loop simulation\n",
    "trajectories = cl_system(data)\n",
    "# plot closed loop trajectories\n",
    "pltCL(Y=trajectories['x'].detach().reshape(nsteps + 1, nx),\n",
    "      R=trajectories['r'].detach().reshape(nsteps + 1, nref),\n",
    "      U=trajectories['u'].detach().reshape(nsteps, nu),\n",
    "      Umin=Umin, Umax=Umax, Ymin=Xmin, Ymax=Xmax,\n",
    "      figname='cl.png')\n",
    "# plot phase portrait\n",
    "pltPhase(X=trajectories['x'].detach().reshape(nsteps + 1, nx),\n",
    "         figname='phase.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6bf37a3",
   "metadata": {},
   "source": [
    "## Evaluation of best model with reference preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd18fbb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# closed-loop simulation with reference preview\n",
    "cl_system_preview.nsteps = nsteps\n",
    "trajectories_with_preview = cl_system_preview(data)\n",
    "pltCL(Y=trajectories_with_preview['x'].detach().reshape(nsteps + 1, nx),\n",
    "        R=trajectories_with_preview['r'].detach().reshape(nsteps + 1, nref),\n",
    "        U=trajectories_with_preview['u'].detach().reshape(nsteps, nu),\n",
    "        Umin=Umin, Umax=Umax, Ymin=Xmin, Ymax=Xmax)\n",
    "pltPhase(X=trajectories_with_preview['x'].detach().reshape(nsteps + 1, nx))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "876817dd",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "In both scenarios, the trained control policies effectively address the reference-tracking problem without violating state or input constraints. Furthermore, incorporating a preview window of future reference signals highlights the predictive capabilities of the trained controller, enabling aticipatory control decisions."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
